# Framework for effective PV system instrumentation focused on fault diagnosis

Edgar Hernando Sepúlveda-Oviedo, Bruno Estibals

► To cite this version:

Edgar Hernando Sepúlveda-Oviedo, Bruno Estibals. Framework for effective PV system instrumentation focused on fault diagnosis. Solar Energy Advances, 2025, 5, pp.100112. 10.1016/j.seja.2025.100112. hal-05251850

HAL Id: hal-05251850

https://hal.science/hal-05251850v1

Submitted on 12 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Framework for effective PV system instrumentation focused on fault diagnosis

Edgar Hernando Sepúlveda-Oviedo<sup>a,b,c</sup>,*, Bruno Estibals<sup>a</sup>

## ARTICLE INFO

**Keywords:**

Photovoltaic systems

Fault detection

Instrumentation framework

Sensor deployment

Data acquisition

## ABSTRACT

A comprehensive framework for instrumentation in photovoltaic (PV) systems is proposed to enhance fault detection accuracy and diagnostic capability across varied PV applications. The framework is structured around four key components: (i) system design considerations, which include PV topology, scale, and sensor placement strategies to maximize detection sensitivity; (ii) data acquisition, detailing sensor selection, sampling rate optimization, and communication protocols adaptable to different configurations; (iii) data management and preprocessing, encompassing storage strategies, data quality control, and normalization pipelines; and (iv) a review of existing monitoring platforms, identifying their limitations for fault-specific diagnostics. Unlike existing standards such as International Electrotechnical Commission (IEC) 61724, which focus on performance monitoring, this framework is explicitly tailored to address diagnostic challenges, offering a fault-oriented perspective on instrumentation design. It provides structured guidelines for aligning spatial resolution, sensor types, and data granularity with the specific needs of fault localization and characterization. The framework also promotes scalable and cost-effective solutions by balancing the trade-offs between instrumentation complexity and diagnostic accuracy. By emphasizing dynamic sampling strategies and preprocessing workflows, the framework supports the development of more responsive and reliable monitoring architectures. This contribution aims to guide practitioners and researchers in improving the resilience, maintainability, and performance of PV systems through more intelligent and diagnosis-ready instrumentation infrastructures.

## 1. Introduction

The global expansion of Photovoltaic (PV) systems reflects the increasing demand for sustainable energy solutions amidst concerns about climate change and fossil fuel depletion. Solar energy, harnessed through PV technology, provides a scalable and environmentally friendly alternative for residential, commercial, and utility-scale applications [1].

By the end of 2023, global PV installations reached a record 447 GW<sub>dc</sub>, representing an 87% increase compared to the 239 GW<sub>dc</sub> installed in 2022 [2]. This growth brought cumulative global capacity to approximately 1.6 TW<sub>dc</sub>, with China contributing 57% of the total installed capacity that year. These figures significantly exceed earlier forecasts and underscore the accelerating pace of PV deployment (Fig. 1).

Looking ahead, projections indicate that total installed PV capacity will exceed 2 TW<sub>dc</sub> by the end of 2024, potentially reaching 5.1 TW<sub>dc</sub> by 2028 if current trends persist [2]. This outlook is presented in Fig. 2.

A key driver of this expansion has been the leadership of China in manufacturing and deployment. In 2023, China alone accounted for more than half of global PV installations, as illustrated in Fig. 3, with other countries such as the United States, India, and Brazil contributing significantly smaller shares.

This explosive growth in capacity is paralleled by increased challenges related to long-term reliability, performance loss, and diagnostic complexity in large-scale installations. These issues highlight the urgent need for robust monitoring frameworks and advanced fault detection systems capable of handling diverse operational conditions and heterogeneous data environments. As photovoltaic deployment accelerates globally, so too does the need to address performance degradation, operational challenges, and diagnostic limitations at scale.

PV systems face numerous challenges that can affect their performance, including shading, dust accumulation, and temperature fluctuations, which can reduce power output significantly. For instance, dust accumulation alone may decrease power generation by as much

<footnote><sup>a</sup> LAAS-CNRS, Université de Toulouse, CNRS, UT, Toulouse, 31031, France</footnote>

<footnote><sup>b</sup> PROMES-CNRS (UPR-8521), Rambla de la thermodynamique, Tecnosud, Perpignan, 66100, France</footnote>

<footnote><sup>c</sup> Université de Perpignan Via Domitia, 52 Avenue Paul Alduy, Perpignan, 66860, France</footnote>

<footnote>* Corresponding author.
E-mail address: ehsepulved@laas.fr (E.H. Sepúlveda-Oviedo).</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 1. Annual solar photovoltaic installed capacity 2000–2023.</figcaption></figure>

Source: Adapted from [2].

<figure><img src="image_4.png" /><figcaption>Fig. 2. World cumulative solar photovoltaic market scenarios 2024–2028.</figcaption></figure>

Source: Adapted from [2].

<figure><img src="image_5.png" /><figcaption>Fig. 3. Top 10 countries by solar PV capacity additions in 2023.</figcaption></figure>

Source: Adapted from [2].

as 8.8% under certain conditions [3]. Moreover, operational challenges like installation errors, component degradation, and suboptimal maintenance practices further risk efficiency losses and, if unaddressed, may lead to system failures [4]. These complexities highlight the importance of systematic fault diagnosis, where well-designed instrumentation frameworks are essential for maintaining optimal PV performance. Such frameworks are vital for effective fault detection and management under diverse operational and environmental conditions.

The motivation for this study arises from the pressing need to develop and enhance instrumentation frameworks that facilitate precise and reliable fault diagnosis across PV systems. Although various diagnosis techniques, including thermal imaging, machine learning, and signal processing, have been proposed in recent years [5], the effectiveness of these techniques remains limited by the quality and design of the underlying instrumentation. Many current PV systems rely on monitoring platforms intended for basic operational oversight rather than dedicated diagnosis tasks, which limits their capacity to detect specific faults that could otherwise reduce energy losses and increase system lifespan. Consequently, there is a strong motivation to advance the design of instrumentation frameworks that are flexible, scalable, and tailored to the specific diagnosis needs of PV systems.

This study contributes by addressing the instrumentation framework necessary for effective PV fault diagnosis. Specifically, it examines the role of existing monitoring platforms in Data Acquisition (DAQ) within PV systems. These platforms facilitate operational monitoring and collect valuable environmental and performance data; however, they are generally not dedicated to fault diagnosis. The distinction between monitoring and diagnosis needs is critical; standard monitoring platforms typically focus on general system oversight rather than capturing the level of detail required for advanced diagnosis. Therefore, this study moves beyond conventional monitoring to define key criteria for designing instrumentation frameworks that enhance fault detection capabilities in PV systems.
The contributions of this study are threefold: First, a critical review of existing PV monitoring platforms is provided, highlighting their limitations in the context of fault diagnosis. Second, a structured framework is proposed to outline essential considerations for effective PV instrumentation, including sensor selection and placement, communication protocols, data handling, and storage solutions. Lastly, the study addresses current challenges in PV instrumentation and suggests improvements to further support diagnosis accuracy and scalability across diverse installations.

Unlike previous surveys and normative standards such as IEC 61724, which primarily provide general guidelines or focus on isolated measurement criteria, the proposed framework offers an integrated, diagnosis-oriented approach to photovoltaic system instrumentation. It bridges the gap between standard compliance and real-world fault detection needs by aligning sensor selection, spatial resolution, sampling strategies, and data processing workflows with specific fault types. This alignment enables a more scalable and application-tailored deployment of monitoring resources in PV installations.

This study is structured as follows: Section 2 reviews relevant studies on PV instrumentation and fault diagnosis. Section 3 presents the proposed framework for PV instrumentation, detailing critical design and operational considerations. Section 4 examines the implications, challenges, limitations, and potential areas for future research related to the framework. Finally, Section 5 offers concluding insights drawn from this study.

## 2. Related work

DAQ systems designed to capture essential environmental and electrical parameters, such as irradiance, temperature, voltage, and current, have been extensively studied [6]. These systems provide valuable data for enhancing PV system monitoring, but the absence of a formalized instrumentation protocol constrains their effectiveness in producing consistent diagnosis outcomes across different installations.

Monitoring and diagnosis processes are crucial for achieving optimal performance and reliability in PV systems. Several studies have developed monitoring systems that track parameters like voltage, current, irradiance, and temperature, with innovations aimed at improving real-time DAQ and overall system efficiency. The IEC 61724-1:2021 standard represents a significant advancement by formalizing performance monitoring practices through standardized measurement methods, data exchange formats, and analysis procedures [7]. Despite its comprehensive scope, this standard prioritizes general monitoring over fault diagnosis and does not specify sensor placements or configurations optimized for fault detection.

To address the monitoring needs of PV systems, cost-effective and accessible solutions leveraging emerging technologies have been proposed. For example, a low-cost virtual instrumentation system using Arduino and Excel enables real-time monitoring of PV panel characteristics [8]. Such systems, however, do not advance fault diagnosis processes. Similarly, Internet of Things (IoT)-based solutions, such as the Trio-PV Monitor, provide scalable and automated monitoring across varied environmental conditions [9]. These solutions primarily focus on general performance tracking, offering limited capabilities for detailed fault diagnosis.

Fault detection methods relying on specific measurements, such as current-voltage (I-V) characteristics, have shown promise in identifying anomalies like partial shading and bypass diode failures [10]. These approaches leverage I-V data obtained from PV string inverters to improve diagnosis accuracy. However, the lack of a comprehensive instrumentation strategy reduces their adaptability across diverse PV installations.

Advanced methodologies incorporating machine learning and expert systems have also been explored. For instance, an expert diagnosis system adjusts criteria based on site-specific conditions to generate real-time maintenance recommendations [11]. This approach enhances

response to detected faults but often lacks protocols for sensor placement or calibration, complicating consistent application across different setups.

Another recent contribution in this area is proposed in [12], which proposes a data-driven framework combining hierarchical clustering, Discrete Wavelet Transform (DWT), and multivariate classification techniques to assess PV module health status. The method begins with time-series data acquired from module-level platforms, which are then clustered using Dynamic Time Warping (DTW) to separate fault types. Diagnostic features are extracted via wavelet decomposition and selected through statistical analysis for training predictive models using Partial Least Squares (PLS) and Linear Discriminant Analysis (LDA). This study demonstrates how advanced feature extraction and classification methods can significantly improve fault detection capabilities in PV systems, emphasizing the importance of high-resolution, module-level instrumentation to supply meaningful input data.

Non-invasive tools like thermographic imaging have gained traction for identifying hotspots indicative of faults in PV systems. Deep learning methods applied to classify thermographic images achieve high accuracy in detecting thermal anomalies [13]. Thermographic imaging, however, functions primarily as a supplementary tool and is rarely integrated into broader real-time fault detection frameworks.

A notable advancement is the use of digital twin models, which simulate real-time conditions in PV systems to identify discrepancies between actual and expected outputs [14]. These models offer real-time fault detection and adaptability to changing conditions through virtual replicas of PV systems. Despite their potential, digital twins lack structured protocols for instrumentation, limiting their generalizability and deployment.

Significant progress has been made in monitoring systems and establishing guidelines for PV instrumentation. Nonetheless, a clear gap remains in the development of frameworks specifically tailored for fault diagnosis. This study addresses this gap by proposing a structured instrumentation framework encompassing targeted sensor placements, diagnosis-oriented DAQ techniques, and advanced processing methods. Through a systematic approach to instrumentation, this work aims to enhance fault diagnosis efficiency across diverse PV configurations, thereby supporting more reliable and efficient PV operations.

## 3. Framework for effective instrumentation in photovoltaic systems

This framework offers a detailed guide to the key aspects required for effective instrumentation in PV systems, including system design, measured variables, data communication, and data management and processing, with a specific focus on improving fault detection and diagnosis accuracy. Instead of proposing a physical setup or conducting experiments, this study offers a structured set of criteria and recommendations for sensor deployment, DAQ, communication protocols, and data processing. By addressing each of these components in detail, the framework supports researchers and practitioners in implementing robust and efficient diagnosis capabilities across diverse PV configurations.

The general structure of the proposed framework is illustrated in Fig. 4. In this figure, the main sections of the framework (rectangles colored in green) are presented, including system design considerations, measured variables, data communication, and data management and processing. These sections, along with their subsections (rectangles colored in pink) and their respective key elements (rectangles colored in yellow), are described in detail in the following parts of this section. Fig. 4 provides a conceptual overview of how these components are structured and interact to improve fault detection and diagnosis in PV systems.

The framework presented in Fig. 4 organizes DAQ into functional layers: measured variables, data communication, and data management and processing. Each layer represents critical criteria that must be
<figure><img src="image_6.png" /><figcaption>Fig. 4. General schematic of the framework for effective PV system instrumentation focused on fault diagnosis. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

evaluated when designing an effective instrumentation system. Within the measured variables, the sensor placement is particularly addressed, which must be optimized to capture accurate and relevant data at multiple levels (e.g., module, string, system), while data communication ensure reliable data transmission. Finally, data management and processing facilitates long-term fault analysis and trend identification, supporting ongoing system maintenance and performance optimization [15].

## 3.1. System design considerations

The topology and scale of a PV system are critical considerations when designing an effective instrumentation setup. Large PV plants, such as utility-scale solar farms, generate extensive volumes of data across numerous arrays of modules and strings, necessitating distributed DAQ points to efficiently manage and process this information.

The variability and intermittency of solar energy production further highlight the need for continuous, high-resolution DAQ. PV systems operate under fluctuating environmental conditions, including changes in irradiance and temperature, which can lead to significant variations in power output. To address these challenges, an instrumentation framework must capture both electrical parameters (e.g., voltage, current, and power) and environmental conditions (e.g., temperature, humidity, wind speed) to provide comprehensive insights into system performance and reliability [16].

In terms of electrical data, PV instrumentation frameworks must monitor both Direct Current (DC) and Alternating Current (AC) parameters at various points in the system. On the DC side, critical variables

include module voltage ($V_U$), string voltage ($V_S$), and current at module and string levels ($I_{ru}$, $I_{FS}$), which are essential for diagnosing common faults such as shading, disconnections, and degradation. On the AC side, monitoring the output current and voltage at the inverter level, as well as parameters such as power factor, harmonic distortion, and frequency stability, enables the detection of inverter failures, grid disturbances, and synchronization issues. These measurements should be collected using sensors such as voltage transducers, Hall-effect current sensors, and power analyzers strategically installed at module terminals, string combiner boxes, and inverter outputs, depending on the required spatial granularity and diagnostic scope.

A key aspect of system design is defining detection levels, which determine the granularity of monitoring within the PV system. Detection can range from array-level monitoring to finer module- or cell-level monitoring. Higher granularity enhances fault localization, enabling precise identification of issues such as partial shading or module degradation. For instance, Kongphet et al. demonstrated that while string-level modeling can detect shading faults, accurate localization and identification — especially under partial shading or aging — require cell-level modeling [17]. Similarly, Lüscher et al. highlighted how limited monitoring granularity in large-scale PV plants can obscure subtle fault signatures, emphasizing the need for more granular data collection to improve diagnosis effectiveness [18]. However, greater granularity also increases the complexity and cost of the instrumentation setup due to the higher number of sensors required and the need for more extensive data processing [15].

This section provides a structured overview of the main considerations for effective instrumentation in PV systems. By carefully evaluating the topology, scale, and detection requirements, practitioners can
<figure><img src="image_7.png" /><figcaption>Fig. 5. Conventional components of a PV system, illustrating modules, strings, and arrays commonly monitored in instrumentation frameworks.</figcaption></figure>

design customized instrumentation frameworks that meet the specific diagnostic needs of their PV installations.

### 3.1.1. *Topology and system size*

The topology and size of a PV system are fundamental parameters that shape the design of an efficient instrumentation framework for fault detection and diagnosis. These factors directly influence key framework components such as sensor placement, DAQ strategies, and communication protocols, which are essential for achieving precise and reliable fault diagnosis.

Fig. 5 provides an overview of the conventional components of a PV system, including the hierarchical structure of modules, strings, and arrays. These structural elements guide the recommendations of the framework for sensor deployment and DAQ strategies, ensuring scalability and adaptability to various system sizes.

In large-scale PV installations, such as utility-scale solar farms, the framework emphasizes the deployment of distributed DAQ points to manage the extensive volumes of data generated across multiple arrays and inverters. This approach aligns with the focus of the framework on scalable instrumentation, allowing efficient processing of high-resolution data to support advanced diagnosis algorithms [19]. In these systems, monitoring at the block or string level is often sufficient for fault detection, as module-level granularity may be cost-prohibitive or unnecessary for operational goals [20].

For smaller installations, such as residential or small commercial systems, the framework prioritizes centralized DAQ to minimize complexity and cost. By focusing on inverter-level monitoring, the framework ensures sufficient fault detection capability without requiring extensive sensor deployment. This design choice supports the objective of the framework to adapt to varying system scales while maintaining diagnosis reliability [21].

System topology, including configurations such as Series-parallel (SP), Total-cross-tied (TCT), and Bridge-linked (BL), plays a critical role in fault resilience and monitoring effectiveness. The framework incorporates recommendations for selecting topologies based on site-specific conditions, such as shading likelihood and environmental factors. For instance, TCT and BL topologies are highlighted for their enhanced tolerance to shading, making them suitable for installations in partially shaded environments [22]. These guidelines aim to optimize both fault detection accuracy and system robustness.

The size of the PV system also determines suitable data communication methods. For large-scale systems, the framework recommends wireless communication protocols such as ZigBee or LoRa to reduce infrastructure costs and support scalability, while addressing latency and

bandwidth constraints to enable real-time fault detection [23]. Conversely, for smaller installations, the framework advocates for wired protocols that offer cost-effective and reliable data transmission.

### 3.1.2. *Detection levels*

Each detection level is associated with certain types of faults that are more effectively diagnosed at that particular granularity. Module-level detection allows for pinpointing faults such as partial shading, degradation, or hot spots within individual PV modules, providing a high degree of fault localization. In contrast, string-level detection is well-suited for identifying faults impacting groups of modules, such as open-circuit or ground faults. System-level detection, which provides a broad view of overall system performance, is useful for identifying major deviations in power output that may indicate widespread issues or grid-related disturbances.

This section details the detection levels, including typical fault types at each level, and examines how they influence the choice of instrumentation and diagnosis techniques. By understanding the diagnosis capabilities and limitations associated with each level, practitioners can design a tailored fault detection framework that balances cost, complexity, and accuracy according to the specific needs of the PV system.

#### A. *Module-level detection.*

Module-level detection is essential for identifying localized faults within individual PV modules. This level of detection provides the highest granularity, allowing for the early diagnosis of issues specific to each module, which may otherwise go unnoticed in coarser detection approaches. At the module level, typical faults include snail trail [24], partial shading [25], hot spots [26], and degradation faults [27], each of which impacts the performance and longevity of the PV module.

Overall, module-level detection enables precise fault localization and targeted maintenance, enhancing the reliability and operational efficiency of PV systems. By implementing module-level monitoring for parameters such as current, voltage, and temperature, it is possible to detect and diagnose faults early, thereby reducing the likelihood of long-term damage and minimizing maintenance costs. The high granularity of module-level detection makes it particularly suitable for large-scale PV installations, where minor faults could cumulatively impact overall system performance.

#### B. *String-level detection.*

String-level detection in PV systems addresses faults that affect groups of modules connected in series, known as strings. This level of detection captures faults that may impact the overall efficiency and
<table><caption>**Table 1**
Detection levels and associated fault types in PV systems.</caption><thead><tr><td>Detection level</td><td>Typical fault types</td></tr></thead><tbody><tr><td>Module-level</td><td>Snail trails, partial shading, hot spots, degradation, bypass diode failure, PID</td></tr><tr><td>String-level</td><td>Open-circuit faults, string imbalance, arc faults, ground faults</td></tr><tr><td>System-level</td><td>MPPT failure, inverter trips, energy yield mismatch, grid disturbances</td></tr></tbody></table>

safety of the PV array, as issues at the string level can significantly reduce power output and lead to potential damage if left unresolved. Common fault types at this level include open-circuit faults [28], string imbalances [29], and ground faults [30], each posing unique challenges for diagnosis systems.

String-level detection thus provides a valuable balance between localized fault identification and overall system monitoring, enabling the early detection of issues that could affect multiple modules. By incorporating robust monitoring techniques, including current differential analysis and voltage pattern recognition, PV systems can maintain optimal performance and ensure safety even under varying environmental conditions and fault scenarios.

### C. System-level detection.

System-level detection in PV systems addresses faults that impact the operational integrity and efficiency of the entire system. Unlike module or string-level faults, system-level faults affect overall power generation and grid stability, posing significant challenges for the reliable integration of PV systems with the utility grid. This level of detection is crucial for identifying deviations in total power output [31] and managing grid-related issues [32] that may arise due to large-scale PV installations.

System-level detection thus provides an overarching view of the health of the PV system and grid compatibility, enabling operators to implement corrective actions for large-scale issues. This level of monitoring is essential for ensuring safe, reliable integration of PV systems into the power grid, particularly as PV penetration continues to increase globally.

### D. Summary of fault types by detection level.

The diagnostic capabilities of each detection level can be better understood when mapped to the specific fault types they are designed to identify. Table 1 summarizes the typical faults most effectively detected at each monitoring level. This overview reinforces the discussion presented in this subsection and provides a concise reference for practitioners to align instrumentation granularity with system requirements.

## 3.2. Measured variables

In PV systems, the measurement of key variables plays a critical role in ensuring accurate fault detection and overall system performance monitoring. These variables can be broadly categorized into electrical, environmental, imaging-based, and structural parameters, each of which contributes unique insights into the operational state of the system. Accurate and reliable DAQ of these parameters is essential for diagnosing faults and optimizing maintenance strategies.

### 3.2.1. Environmental variables

Environmental variables such as irradiance, temperature, wind, and humidity play a critical role in PV system diagnostics by helping distinguish environmental influences from internal faults. Their accurate measurement — using properly classified, mounted, and calibrated sensors — is essential for performance evaluation and fault detection, as defined in standards like IEC 61724.

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Examples of irradiance sensors (Pyranometers).</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Examples of module temperature probes.</figcaption></figure>

### A. In-plane irradiance.

This is the most critical environmental input, directly influencing power output. It is typically measured using pyranometers or PV reference devices installed coplanar with the module plane to replicate operating conditions [7]. Thermopile pyranometers offer high accuracy (Fig. 6).

### B. Ambient and module temperature.

These affect both efficiency and degradation rates. According to IEC 61724, sensors must have accuracy within ±1 °C and be mounted behind a representative module cell using thermally conductive adhesives (see Fig. 7) [7].

### C. Wind speed and direction.

Wind contributes to natural cooling and affects dust accumulation. Anemometers and wind vanes should be installed at module height. Recommended accuracy is ±0.5 m/s for wind speed and ±5° for direction [7]. These are typically integrated into weather stations (see Fig. 8).

### D. Humidity.
<table><caption>**Table 2**
Summary of environmental variables for PV system monitoring.</caption><thead><tr><th>Variable</th><th>Acronym</th><th>Unit</th><th>Typical sensor type</th><th>Accuracy</th></tr></thead><tbody><tr><td>In-plane irradiance</td><td>G</td><td>W/m²</td><td>Pyranometer/PV reference device</td><td>±5 W/m²</td></tr><tr><td>Ambient temperature</td><td>T<sub>amb</sub></td><td>°C</td><td>Thermocouple/RTD</td><td>±1 °C</td></tr><tr><td>Module temperature</td><td>T<sub>mod</sub></td><td>°C</td><td>Surface-mounted probe</td><td>±1 °C</td></tr><tr><td>Wind speed</td><td>WS</td><td>m/s</td><td>Cup/ultrasonic anemometer</td><td>±0.5 m/s</td></tr><tr><td>Wind direction</td><td>WD</td><td>degrees</td><td>Wind vane</td><td>±5°</td></tr><tr><td>Humidity</td><td>RH</td><td>%</td><td>Capacitive/resistive sensor</td><td>±3% RH</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Examples of wind sensors.</figcaption></figure>

High humidity can degrade insulation and accelerate aging. Capacitive or resistive sensors housed in protective enclosures are commonly used and should be placed in representative microclimatic conditions [33].

To provide a concise overview and aid practical implementation, Table 2 summarizes the main environmental variables discussed above, including their units, acronyms, typical sensors, and accuracy specifications.

### 3.2.2. Electrical variables

Electrical variables are essential for monitoring performance and diagnosing faults in PV systems. These parameters, which primarily include voltage and current, provide critical insights into the energy conversion process and the operational status of the system. Accurate measurement of these variables is fundamental for identifying anomalies such as open circuits, module degradation, and imbalances within the PV array. Power can be derived from voltage and current measurements; however, direct measurement may be preferred in specific applications requiring high accuracy or compliance with certain international standards.

#### A. Voltage and current.

Voltage and current are fundamental electrical parameters in PV systems. These variables provide critical insights into the operational status of the system and are essential for diagnosing issues such as open circuits, module degradation, or wiring faults. Voltage parameters include module voltage ($V_U$), string voltage ($V_S$), and array voltage ($V_A$), while current parameters comprise module current ($I_{ru}$), string current ($I_{FS}$), and array current ($I_A$) [34].

The use of sensors such as shunts, Hall effect sensors, Rogowski coils, and voltage transducers is common for voltage and current monitoring. Shunts offer a cost-effective solution for current measurements at the module or string level, whereas Hall effect sensors and Rogowski coils provide non-contact options with high accuracy [35]. Voltage

<table><caption>**Table 3**
Summary of electrical variables for PV system monitoring.</caption><thead><tr><th>Variable</th><th>Symbol</th><th>Unit</th><th>Recommended accuracy</th><th>Typical sensor</th></tr></thead><tbody><tr><td>Module voltage</td><td>V<sub>U</sub></td><td>V</td><td>±1%</td><td>Voltage transducer</td></tr><tr><td>String voltage</td><td>V<sub>S</sub></td><td>V</td><td>±1%</td><td>Voltage transducer</td></tr><tr><td>Array voltage</td><td>V<sub>A</sub></td><td>V</td><td>±1%</td><td>Voltage transducer</td></tr><tr><td>Module current</td><td>I<sub>ru</sub></td><td>A</td><td>±1%</td><td>Shunt, Hall sensor</td></tr><tr><td>String current</td><td>I<sub>FS</sub></td><td>A</td><td>±1%</td><td>Hall effect sensor</td></tr><tr><td>Array current</td><td>I<sub>A</sub></td><td>A</td><td>±1%</td><td>Rogowski coil, Hall sensor</td></tr></tbody></table>

transducers are recognized for their precision in capturing fluctuations critical for fault diagnosis [36].

Voltage sensors should be installed at strategic locations, including module outputs, string terminations, and inverters, to capture representative electrical data. Current sensors are optimally placed at string-level junction boxes and inverter inputs to identify anomalies caused by shading, degradation, or wiring issues. In larger installations, additional sensors may be positioned at block or array levels to balance cost and diagnostic accuracy.

This study operates under the hypothesis that measuring only voltage and current is sufficient for effective fault diagnosis in PV systems. Power, while a derived parameter, can be calculated in real-time using voltage and current values, eliminating the necessity of dedicated power sensors. However, for applications where direct power measurement is required, international standards, such as IEC 61724, recommend a power sensor accuracy within ±2% [34]. This ensures the reliability of collected data and complements voltage and current measurements when required.

To facilitate quick reference and support practical implementation, Table 3 summarizes the key electrical variables discussed, including their symbols, units, recommended accuracy, and typical sensing methods.

### 3.2.3. Imaging-based measurements

Imaging-based measurements are essential tools for diagnosing and monitoring faults in PV systems. These techniques rely on capturing visual or thermal data to identify anomalies such as micro-cracks, hot spots, and material degradation that may not be detected through electrical or environmental measurements alone. By providing a non-invasive and detailed assessment of PV module conditions, imaging-based methods enhance the accuracy and reliability of fault detection.

Common imaging techniques include Infrared (IR) imaging, Electro-luminescence (EL), and Photoluminescence (PL), each offering unique capabilities to evaluate the structural and operational integrity of PV modules. These methods are particularly effective for detecting subtle defects that could compromise efficiency and system longevity. Advances in imaging technology have also enabled high-resolution assessments that support predictive maintenance strategies, reducing downtime and operational costs. Regular imaging intervals are recommended to ensure timely fault detection; for instance, annual or bimannual imaging is generally sufficient for well-maintained systems, but higher frequencies may be required in harsh operational environments or aging systems [37]. Imaging-based measurements are thus integral to comprehensive monitoring frameworks, complementing electrical and environmental data to ensure optimal PV performance.
<figure><img src="image_11.png" /><figcaption>Fig. 9. Example of an infrared image for diagnosis [38].</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 10. Example of an electroluminescence image for diagnosis [45].</figcaption></figure>

### A. Infrared imaging.

IR is a widely used non-contact technique for monitoring temperature distribution across PV modules. By capturing thermographic data, IR imaging enables the detection of localized hot spots that may indicate faults such as bypass diode failures, partial shading, or increases in internal resistance [35]. These thermal anomalies often correlate with performance losses, allowing operators to identify and address potential issues early. An example of this type of images is presented in Fig. 9.

IR cameras and thermal imagers are particularly effective in detecting temperature differentials that standard electrical measurements may overlook [37]. This capability provides valuable insights into module degradation, mechanical stress, and potential failure risks. For optimal results, IR cameras should be positioned to capture a clear and unobstructed view of the PV module surface. Fixed-angle cameras are suitable for small-scale systems, while scanning systems or drones equipped with IR cameras are increasingly utilized in large installations, enabling efficient imaging across extensive arrays [39]. Drone-mounted IR systems provide high mobility and comprehensive coverage, particularly in utility-scale PV plants, where manual inspections may be impractical [14].

The frequency of IR imaging is a critical factor for effective fault detection. Routine imaging is recommended every 6 to 12 months for operational systems, depending on environmental conditions and system size [40]. However, in areas with frequent environmental stressors, such as extreme temperature fluctuations or high soiling rates, more frequent assessments, such as quarterly imaging, may be beneficial [37]. Automated IR systems integrated into monitoring frameworks allow for continuous or on-demand imaging, enhancing the responsiveness of maintenance strategies.

IR imaging is also integrated into automated fault detection frameworks, where advanced image processing techniques and machine learning algorithms enhance the identification of anomalies. By combining accurate sensor placement with data-driven diagnostic tools, operators can achieve high-resolution assessments of thermal anomalies and optimize maintenance strategies. This integration of IR imaging improves fault detection accuracy and supports the overall reliability and efficiency of PV systems.

### B. Electroluminescence and photoluminescence.

EL and PL imaging are advanced diagnostic techniques used to detect micro-cracks, material defects, and other structural anomalies in PV modules. These methods provide high-resolution insights into module integrity, making them indispensable tools for both quality control and ongoing system maintenance [41].

EL imaging involves powering PV modules under low-light conditions, which causes the cells to emit IR light. Defects such as micro-cracks, Potential-Induced Degradation (PID), and cell fractures disrupt the uniform emission pattern, allowing a detailed visualization of internal damage that could impair module performance [42]. This technique

is widely used for quality control during module manufacturing and serves as a periodic diagnostic tool in operational systems.

PL imaging, similar to EL, identifies material inconsistencies without requiring electrical power. Instead, laser or Light-Emitting Diode (LED) illumination excites the module surface, highlighting defects at a sub-cell level [43]. PL imaging is particularly effective in detecting early-stage material fatigue or damage, which could impact future performance. Its non-contact nature makes it suitable for rapid assessments, especially in systems where electrical power for EL imaging may not be readily available.

The frequency of EL and PL imaging depends on system conditions and operational requirements. For new installations, initial imaging during commissioning ensures module quality and detects manufacturing defects [41]. Periodic imaging every 1 to 2 years is recommended for operational systems, with more frequent assessments in regions with high environmental stress or for systems exhibiting performance deviations [41]. Advanced setups with automated imaging capabilities provide on-demand diagnostics, reducing downtime and maintenance costs. An example of this type of images is presented in Fig. 10.

In stationary setups, imaging devices should be aligned perpendicular to the module surface to capture uniform and high-resolution images. For large-scale PV installations, automated scanning systems or robotic arms equipped with imaging sensors are increasingly employed, enabling efficient inspection of extensive arrays. Portable EL and PL imaging setups are also utilized for on-site diagnostics, offering flexibility and mobility for targeted inspections [44].

### 3.2.4. Structural and deformation measurements

Structural and deformation measurements are critical for assessing the mechanical integrity and long-term durability of PV modules and systems. These measurements focus on identifying physical stresses, material degradation, and structural defects that may develop over time due to environmental conditions, operational loads, or manufacturing inconsistencies. By providing insights into mechanical performance, structural and deformation measurements complement electrical and environmental monitoring, ensuring the overall reliability of PV installations.

Advanced techniques such as ultrasonic sensing, Digital Image Correlation (DIC), and strain gauge monitoring are employed to detect micro-cracks, delamination, and mechanical deformations that could compromise module efficiency and lifespan. These non-invasive methods enable detailed assessments without interfering with module operation, making them suitable for both laboratory testing and field applications. Structural and deformation measurements are integral to proactive maintenance strategies, allowing early detection of potential failures and reducing operational risks in PV systems.
### A. Ultrasonic sensors.

Ultrasonic sensors are employed as diagnostic tools to detect structural defects in PV modules, such as micro-cracks, delamination, and adhesion issues. High-frequency sound waves are emitted, traveling through the module and reflecting off internal surfaces, providing detailed insights into structural integrity. Variations in the reflected signals are analyzed to identify defects that may compromise module performance or durability over time [46].

Recent advancements in ultrasonic sensing have improved the precision and applicability of these techniques in diagnosing mechanical issues. For example, studies like Nakatsuji et al. (2023) have demonstrated enhanced accuracy of ultrasonic pulse measurements using Surface Plasmon Resonance (SPR) sensors, which allow for precise detection of subtle material discontinuities. These methods hold potential for improving the early identification of micro-cracks and other defects in photovoltaic systems [47].

This technique is particularly employed for the detection of encapsulation and adhesion issues caused by prolonged exposure to environmental stressors, including temperature fluctuations, moisture, and mechanical loads. Ultrasonic sensing is recognized as non-invasive, highly sensitive, and suitable for both laboratory testing and on-site inspections. The Resonance Ultrasonic Vibrations (RUV) method, for example, has been applied for detecting micro-cracks in silicon wafers by analyzing deviations in resonance frequency and bandwidth to determine the presence and severity of defects [46].

To ensure effective diagnostics, regular ultrasonic measurements are recommended. The specific frequency depends on the environmental and operational conditions of the system; however, measurements every 6 to 12 months are generally sufficient for most PV installations to monitor the development of structural defects and mitigate long-term risks [47].

Sensors should be positioned to ensure optimal contact with the module surface, typically at multiple points, to capture a comprehensive structural profile. For large-scale applications, automated scanning systems or robotic platforms equipped with ultrasonic sensors are implemented to enable efficient inspection of extensive arrays. These setups facilitate the detection of hidden defects and support proactive maintenance strategies, contributing to the improvement of long-term reliability in PV installations.

### B. Digital image correlation (DIC).

DIC is a non-destructive optical technique for measuring surface deformations in PV modules under stress. High-resolution imaging is used to track micro-deformations and map mechanical strain and stress points caused by thermal expansion, wind loads, or installation forces. This method provides detailed insights into the mechanical behavior of modules, identifying early signs of mechanical degradation [48].

The DIC process involves capturing sequential images of the module surface under various loading conditions. Advanced algorithms analyze these images to compute deformation fields with high precision [49]. This technique helps operators assess the effects of environmental stressors and predict potential mechanical failures.

For effective diagnostics, the frequency of DIC assessments depends on environmental conditions and operational stress levels. It is generally recommended to conduct DIC measurements at least annually during routine maintenance checks. However, in systems exposed to high-stress environments or showing preliminary signs of degradation, bi-annual or quarterly measurements are advisable [50]. Studies suggest that DIC inspections should be timed to coincide with periods of peak operational stress, such as extreme weather events, to capture relevant mechanical strain data [51]. Additionally, continuous monitoring using advanced automated DIC systems may be considered in large-scale installations for real-time deformation analysis [52].

To ensure accuracy, cameras should be positioned to capture the full module surface from optimal angles. For large-scale PV systems, robotic platforms equipped with DIC systems automate the process, facilitating

efficient monitoring across extensive arrays. Portable setups are also available for targeted inspections of specific modules [53]. The integration of real-time imaging technologies further enhances diagnostic capabilities, enabling precise tracking of dynamic mechanical responses and improving the reliability of predictive maintenance strategies [54].

### 3.3. Data communication

Effective data communication is essential in PV systems, providing the backbone for real-time monitoring and fault diagnosis. A reliable data communication infrastructure ensures that information from various sensors is promptly transmitted to monitoring and analysis platforms, facilitating proactive fault management and optimizing PV system performance. This section addresses key aspects of data communication critical for PV fault detection, including the choice of communication protocols and the impact of sampling rate on data accuracy and diagnosis responsiveness [55].

In PV systems, the selection of appropriate communication protocols depends on the scale and complexity of the installation. For smaller systems, such as residential setups, wired networks like Ethernet are often preferred due to their high reliability, low latency, and straightforward integration with existing infrastructure. Ethernet is particularly advantageous in compact installations where cabling requirements are minimal, ensuring seamless data flow with minimal interference [56].

In contrast, for large-scale PV installations, wireless communication protocols offer significant benefits. Wireless options, such as ZigBee, LoRa, and Wi-Fi, provide increased flexibility and scalability, which are crucial for extensive solar farms where wired connections may be logistically or financially impractical [57]. ZigBee, with its low power consumption and efficient data transfer, supports numerous sensors across expansive installations, making it a popular choice for utility-scale PV systems [58]. LoRa, with its long-range and low-power capabilities, is particularly suitable for remote PV systems where continuous monitoring is necessary, but infrastructure constraints exist [59]. Wi-Fi networks, while more power-intensive, provide higher data rates and are commonly deployed in smaller commercial systems or installations where Wi-Fi infrastructure is readily available [60]. Additionally, 4G/5G networks enable remote monitoring and cloud-based data management for large installations in areas without fixed communication lines, enhancing scalability and accessibility.

The choice of wired or wireless communication must consider the specific requirements of the PV installation. For small-scale systems, wired solutions typically offer a cost-effective and reliable approach, ensuring consistent data transmission without the need for complex network configurations. However, for large-scale installations, wireless communication is often indispensable due to its flexibility and ability to cover wide geographic areas.

Another critical consideration in PV data communication is the sampling rate, which influences the resolution and relevance of collected data. Higher sampling rates allow for finer detail, capturing transient behaviors and quick variations that can signify faults. However, higher sampling also requires greater bandwidth and storage, potentially increasing system costs and energy consumption. Studies indicate that sampling rate optimization should consider the specific fault detection requirements, balancing between detailed monitoring and resource efficiency [61]. Adaptive sampling strategies that adjust data collection frequency based on real-time system conditions have been proposed to enhance data relevance while conserving bandwidth and power [62].

The following subsections provide an in-depth analysis of communication protocols and sampling rate strategies, emphasizing their roles in establishing an efficient data communication framework for fault diagnosis in PV systems.
### 3.3.1. Communication protocols

The selection of suitable communication protocols is critical to maintaining reliable and efficient data flow within PV systems, especially given the need for continuous monitoring under dynamic environmental conditions. Effective communication protocols support high-frequency data transfer, robustness against interference, and reliable real-time monitoring essential for proactive fault detection and management in PV systems. These protocols are chosen based on various criteria, including data rate, latency, reliability, security, and the scalability of the network infrastructure. In PV systems, communication protocols are generally divided into two primary categories: wired and wireless, each with unique advantages and applications based on the scale of the system, location, and monitoring requirements.

Wired communication protocols are often used for connecting sensors to controllers or controllers to local data storage. These protocols are known for their stability, low latency, and high data throughput, making them ideal for applications requiring steady data transmission over fixed distances. For example, Modbus is commonly employed in PV systems to link monitoring devices to Supervisory Control and Data Acquisition (SCADA) platforms due to its simplicity and robust performance, allowing seamless connectivity between equipment [63]. Ethernet is utilized for connecting controllers to local data processing systems or SCADA units, providing high-speed, low-latency data transmission. This makes Ethernet particularly advantageous in large installations where stable and extensive data communication is required [56]. Power Line Communication (PLC), which transmits data through existing electrical infrastructure, is a practical choice for short-to medium-distance connections in PV plants where wireless signals may face interference or coverage limitations [64].

In addition to digital protocols, analog signals from sensors can be directly transmitted to the controller using wired connections. This approach is particularly useful when sensors output analog signals such as voltage or current. These signals are then digitized by the Analog-to-Digital converter (ADC) within the controller, enabling accurate DAQ while maintaining a simple and robust infrastructure.

Wireless communication protocols are widely used for connecting controllers to cloud-based platforms or for establishing networks in remote or hard-to-access areas. Wireless options such as ZigBee, LoRa, and Wi-Fi provide the flexibility needed for expansive PV systems. ZigBee is commonly applied for sensor-to-controller connections due to its low power consumption and ability to support mesh networks, making it suitable for large-scale arrays with numerous sensors [58]. LoRa is particularly effective for controller-to-cloud communication in remote installations, as it enables long-range, low-power data transmission over several kilometers [59]. Wi-Fi, known for its high data transfer rates, is often used for transmitting real-time monitoring data from controllers to cloud-based platforms or local servers [60]. For long-distance data transfer, 4G/5G networks can complement these wireless options, ensuring reliable communication in areas without wired connectivity.

Security and interoperability are critical considerations when deploying communication protocols. Encryption is necessary for safeguarding data integrity, particularly for wireless communication that are more susceptible to interception. Scalability is essential in larger installations, where networks must accommodate hundreds or thousands of sensors. Interoperability among devices from different manufacturers ensures seamless integration of sensors, controllers, and data management platforms [65].

Fig. 11 presents a general overview of communication protocols and how they interact among the different elements of the diagnostic-oriented monitoring system.

### 3.3.2. Sampling rate

The sampling rate is a critical factor in PV monitoring systems as it influences both the accuracy of fault detection and the efficiency of data storage and transmission. Higher sampling rates capture finer temporal details, enabling the detection of rapid changes in PV system parameters such as current and voltage. However, higher rates also lead to increased data redundancy, elevated storage requirements, and higher computational demands. Balancing the sampling rate to achieve optimal diagnosis precision while managing resource constraints is essential for efficient PV monitoring, especially in large-scale installations [61].

Sampling rate selection in PV systems should consider both the type of fault detection required and the operational conditions of the installation. For instance, dynamic conditions, such as those influenced by sudden shading or partial module failures, may benefit from higher sampling rates to detect transient faults accurately. Research suggests that adaptive sampling strategies, which adjust the rate based on real-time data requirements, can enhance the efficiency of DAQ while reducing the load on storage and computational systems [62]. In this approach, the system dynamically increases the sampling frequency during critical periods, such as sudden environmental changes or suspected fault occurrences, and reduces it during stable periods. This method optimizes data fidelity while conserving storage and communication resources.

Recent studies support these findings. For example, Chen and Wang (2018) demonstrated that incorporating a selective sampling strategy enhanced the speed and accuracy of fault localization by capturing detailed temporal variations in voltage and current signals [66]. Similarly, Rivai et al. (2020) showed that an adaptive-sampling-rate method in I-V curve tracers improves measurement resolution and facilitates more accurate fault comparisons among PV modules [67]. These findings support the premise that finer temporal granularity contributes to more sensitive and reliable diagnostics, justifying the implementation of dynamic or high-frequency sampling in advanced PV monitoring architectures.

Standards and guidelines provide valuable insights into recommended sampling intervals for PV systems. The IEC 61724 standard advises a sampling interval of less than 3 s for key parameters like irradiance, temperature, and electrical output. If hardware limitations exist, a sampling rate of up to 1 min may be acceptable, particularly for less dynamic parameters. For environmental variables with slower changes, such as humidity or soiling, a sampling interval of up to 5 min is often sufficient, as these parameters do not require rapid updates for effective monitoring [34].

Various technical reports align with these standards but provide additional recommendations. The International Energy Agency (IEA) recommends DAQ at intervals of 1 s or faster, with averaged values stored every 5–15 min for long-term analysis. This practice supports data quality by ensuring that even rapidly changing conditions are captured, while averaged data minimizes storage demands [68]. Similarly, the best O&M practices report by SolarPower Europe suggests irradiance measurements at intervals no longer than 15 min for general monitoring and as low as 1 min for detailed analysis [69]. For more frequent measurements, such as those capturing DC voltage and current inputs, a maximum interval of 1 s is recommended, particularly if power calculations are derived from these readings.

Further recommendations for adaptive sampling in PV systems highlight the importance of selecting a sampling interval that aligns with the time constant of the measuring instrument. Studies suggest that the sampling interval should be carefully chosen to optimize accuracy in irradiation measurements, with intervals around ten seconds recommended to ensure precision, particularly in variable weather conditions [70]. In cases where PV system performance is highly sensitive to environmental fluctuations, such as in regions with frequent cloud cover or rapidly changing weather, a more frequent sampling rate may be beneficial to capture transient phenomena that impact power output.
<figure><img src="image_13.png" /><figcaption>Fig. 11. Example of different communication protocols for PV Instrumentation in fault diagnosis.</figcaption></figure>

Higher sampling rates can enhance diagnosis sensitivity; they may also lead to unnecessary data accumulation and increased operational costs. Implementing a moderate sampling rate that adjusts to the specific requirements of the PV system operational environment and fault diagnosis needs can ensure effective monitoring without overburdening storage and processing resources. Balancing the sampling rate with data fidelity needs and system capacity is essential for maintaining robust and efficient PV monitoring architectures, which can ultimately reduce maintenance costs and improve system uptime through accurate, real-time fault detection.

## 3.4. Data management and processing

Effective data management and processing are critical for optimizing PV system performance and ensuring precise fault detection. In PV systems, a continuous flow of data is generated from various sensors monitoring electrical parameters, environmental conditions, and overall system status. This extensive data stream necessitates a structured approach for storage, processing, and utilization, all of which support diagnosis algorithms and comprehensive performance analysis. A well-defined data management and processing framework not only enables real-time fault detection but also facilitates long-term performance evaluation and predictive maintenance.

This section presents the key components of data management and processing within PV systems, highlighting the essential roles of controllers, efficient data storage strategies, preprocessing and data quality maintenance, and the integration of existing DAQ systems. The controller plays a central role by managing the flow of data, coordinating data collection from sensors, and ensuring timely delivery to processing units. This orchestrated approach minimizes latency and ensures that data is accessible for both real-time analysis and historical assessment.

Robust data storage solutions are also fundamental in handling the high-frequency data typical of modern PV monitoring systems. These solutions are designed to manage current data demands while retaining historical data to support trend analysis and optimize system performance. Additionally, the preprocessing stage and data quality controls ensure that the collected data is clean, reliable, and ready for diagnosis and analytical processes. Proper data cleaning, outlier detection, and validation are crucial to maintaining the integrity of data used for fault diagnosis and performance optimization.

Lastly, this section explores existing DAQ systems employed in PV applications, examining their capabilities, advantages, and limitations. Understanding these systems is essential for assessing how they contribute to or potentially hinder effective fault detection and data-driven decision-making. By addressing these critical aspects of data management and processing — encompassing controllers, storage, preprocessing, and the use of established acquisition systems — this framework aims to enhance PV system diagnosis reliability and efficiency. This approach ultimately contributes to improved system performance, reduced operational costs, and more effective maintenance strategies.

### 3.4.1. Controller

In PV monitoring systems, the controller functions as the central interface between sensors and the data management infrastructure, facilitating DAQ, managing sensor communication, and directing data to processing units. This functionality is crucial for accurate real-time monitoring, effective fault detection, and long-term performance analysis. By managing the data flow from various sensor nodes, the controller ensures that essential information is consistently collected, processed, and made accessible for diagnosis or operational decisions [36].

Controllers in PV systems are generally implemented using microcontrollers or DAQ boards, each with distinct advantages. Microcontrollers are frequently selected for smaller scale PV setups due to their cost-effectiveness and simplicity. The resolution of the ADC within the microcontroller significantly influences data accuracy, with higher resolutions (typically 12-bit or greater) enhancing the detection of subtle variations in PV system parameters, such as voltage and current fluctuations [71]. This sensitivity is essential for fault detection, as minor changes in these parameters may indicate emerging issues.

Conversely, DAQ boards are often deployed in larger or more complex PV installations that require advanced data integration and processing capabilities. Although these boards generally involve higher costs, they offer expanded functionality, support higher data throughput, and accommodate multiple sensor configurations [72]. This makes them well-suited for large-scale PV plants where comprehensive data coverage and higher sampling rates are necessary to ensure detailed monitoring.

The number of controllers required in a PV installation depends on the scale and complexity of the system. For small-scale systems, a single controller may suffice to manage both electrical and environmental data streams. In larger or utility-scale installations, separate controllers for electrical and environmental data are recommended to handle higher data volumes and ensure efficient processing [36]. The division of responsibilities enables more precise monitoring and improves fault detection sensitivity. Additionally, the modularity of controller deployment facilitates scalability, allowing the system to accommodate expansions or additional monitoring needs.

The selection of a controller involves various considerations, including ADC resolution, data transmission capabilities, and compatibility with specific communication protocols [71]. For instance, low-cost microcontrollers with wireless communication capabilities like ZigBee are effective for smaller systems, while DAQ boards with high sampling rates and extensive sensor compatibility are preferable for large-scale installations [72]. Additionally, the programming environment plays a significant role; controllers for PV systems are commonly programmed in languages such as C, MATLAB, or LABVIEW. Although costly, LABVIEW is widely used in academic and industrial settings due to its comprehensive measurement and control functionalities, which support both real-time monitoring and extensive data analysis.
Controllers also differ in terms of power supply requirements and data transmission flexibility. For remote PV systems, wireless communication protocols like ZigBee, coupled with low-power controllers, present an energy-efficient solution that enhances system longevity [71]. Wireless Sensor Networks (WSNs) reduce infrastructure costs and improve scalability, enabling controllers to support a larger array of sensors across geographically distributed installations.

Selecting an appropriate controller requires balancing key attributes to ensure the diagnosis sensitivity, operational efficiency, and data reliability of the system. Advanced controllers equipped with high-resolution ADCs, versatile communication options, and robust data management capabilities support not only real-time fault detection but also predictive maintenance strategies. These features collectively establish the controller as an integral part of a PV system instrumentation framework, contributing to improved operational performance and reduced maintenance costs throughout the system lifecycle.

### 3.4.2. Data storage

Effective data storage is a critical component in PV monitoring systems, where diverse data types, such as electrical measurements, environmental parameters, and imaging data, are continuously generated. The storage infrastructure must adapt to the nature of the data, accommodating variations in data frequency, volume, and type. High-resolution electrical data may require rapid sampling intervals to capture transient behaviors, whereas imaging data demands substantial storage capacity due to its high file size and periodic nature [36].

In smaller or decentralized PV installations, local storage solutions, such as Secure Digital (SD) cards or onboard memory modules, are often sufficient for storing low-frequency data. Conversely, for larger or grid-connected systems, centralized or cloud-based solutions are preferred to handle high-frequency and high-volume data streams [73]. Cloud storage also enables seamless access for long-term analysis and scalability, adapting to the growing monitoring needs of PV systems. For imaging data, particularly from infrared thermography or electroluminescence assessments, specialized storage strategies are required to manage large file sizes while maintaining accessibility [60].

The choice of data storage also depends on the sampling rates and resolution requirements. High-frequency electrical data storage retains detailed performance metrics essential for detecting transient faults. However, excessive data resolution can strain storage resources. To optimize storage, aggregation techniques such as averaging or downsampling are often applied, preserving critical information without overloading the system [36]. Adaptive storage strategies dynamically adjust resolution based on operational priorities, ensuring efficient use of storage while capturing essential events.

When imaging data or high-resolution fault diagnostics are involved, gateways may be employed to bridge local storage and cloud platforms. These gateways facilitate the transfer of large datasets while optimizing bandwidth usage, particularly in remote PV installations with limited connectivity [60]. This approach enhances the accessibility of high-volume data for advanced analytics, such as machine learning-based fault prediction models.

### 3.4.3. Preprocessing and data quality

The preprocessing and data quality assurance stages are essential components within the data management framework of PV monitoring systems. A general schematic summarizing this preprocessing and data quality framework is presented in Fig. 12.

Stages presented in Fig. 12 ensure that data, originating from a variety of sensors and sources, is accurate, consistent, and properly prepared for subsequent analysis. The process initiates with raw DAQ (step A), encompassing both time-series data (e.g., electrical measurements such as voltage and current, as well as environmental data like irradiance and temperature) and non-time-series data (e.g., imaging and structural data). This differentiation (step B) allows the data to

be routed through tailored preprocessing pathways optimized for the unique characteristics of each type.

Time-series data undergoes a structured pipeline beginning with data cleaning (step C-1), where outliers and noise are filtered to enhance reliability [35]. Typically, this involves statistical techniques such as removing values beyond three standard deviations from the mean, applying median or low-pass filters to smooth sensor noise, and enforcing plausibility checks based on physical bounds (e.g., irradiance $\ge 0$ and $\le 1200$ W/m$^2$). Missing values are interpolated using linear or spline methods, or reconstructed with Singular Spectrum Analysis (SSA) [74].

Subsequently, normalization (step C-2) is applied to standardize units and scales across various sensors, facilitating interoperability and consistency [75]. This is typically performed using min-max scaling (to [0,1] range) for environmental data and z-score normalization (mean = 0, standard deviation = 1) for electrical parameters. These approaches help ensure numerical stability in downstream algorithms and enable fair comparison across heterogeneous data streams.

Time-series validation (step C-3) follows, involving checks against operational thresholds and calibration to ensure the integrity of temporal trends. This step is critical for detecting long-term deviations indicative of performance degradation or faults [20].

Non-time-series data, such as imaging and structural measurements, require distinct preprocessing techniques. Imaging data undergoes enhancement (step D-1) to improve visual clarity, such as noise reduction and contrast adjustment [37]. Feature extraction (step D-2) is then performed to identify critical attributes, including hot spots, cracks, and deformations, which are essential for diagnosing mechanical or thermal issues [39]. Validation and annotation (step D-3) compare these features against baseline data, enabling anomaly detection and supporting predictive maintenance [41].

Following individual preprocessing pathways, both data streams converge at a unified quality assurance stage (step E-1). This integration ensures that inconsistencies are identified and rectified in a comprehensive manner. Anomaly detection (step E-2) algorithms then leverage statistical methods for time series data and machine learning techniques for image and structural data sets to detect the outliers either for removal or to report them as an anomaly in the system [21].

Advanced methodologies have also been explored to address missing or erroneous data, employing techniques such as time-series reconstruction and inference. For example, methods like singular spectrum analysis and interpolation have demonstrated efficacy in recovering corrupted photovoltaic datasets [74]. Similarly, machine learning-based models enhance the robustness of preprocessing by automatically identifying and flagging anomalies [76].

A final data quality check (step E-3) verifies that processed data meets operational thresholds, rendering it ready for storage or advanced analysis (step F).

### 3.4.4. Existing data acquisition systems

Existing DAQ systems in PV monitoring provide pre-configured solutions for effective data collection, processing, and storage, supporting seamless data management across a variety of PV configurations. These systems are typically composed of integrated controllers, sensors, and data storage units, which facilitate the real-time monitoring of PV installations. Modular systems with programmable controllers are particularly beneficial in large PV installations, where data from numerous sensors must be processed and analyzed to meet diverse diagnosis requirements [73].

Historically, the development of DAQ systems for PV applications has progressed from basic setups to sophisticated, high-resolution systems. Early DAQ systems, like those introduced by Blaesser [77], primarily facilitated on-site DAQ for measuring and analyzing PV performance. However, these initial systems were costly, with setup costs often exceeding 10% of the total PV plant investment, limiting their accessibility for smaller installations. Over time, as hardware costs
<figure><img src="image_14.png" /><figcaption>Fig. 12. Preprocessing and data quality framework for mixed data types.</figcaption></figure>

decreased, more affordable DAQ systems became feasible, including systems that monitored key variables such as solar radiation and environmental conditions, using 8-bit microcontrollers and Electrically-Erasable Programmable Read-Only Memory (EEPROM) storage—an efficient design for remote locations focused on low-power, cost-effective data collection [78].

In larger PV installations, more advanced DAQ boards equipped with high-resolution ADCs support the complexity of multi-sensor configurations. These systems allow higher sampling rates, which are essential for precise monitoring in extensive PV arrays [79]. Additionally, WSNS using protocols such as ZigBee and LoRa have enhanced the scalability of DAQ systems by enabling low-power data transmission across wide PV fields while reducing cabling infrastructure costs. This feature is particularly valuable in large-scale PV plants, where wired setups could be logistically challenging and expensive [80].

Software platforms like LABVIEW, although relatively expensive, are widely used in PV research for their advanced measurement and control functionalities. Programming environments such as C and MATLAB also provide robust options for managing data in microcontroller-based systems, enabling real-time customization of monitoring interfaces [73]. The choice of software can significantly impact ease of customization for specific diagnosis and operational needs.

Table 4 summarizes various DAQ systems historically developed for PV applications, detailing their configurations and features.

Despite the capabilities of these DAQ systems for effective data monitoring in PV installations, several limitations emerge when considering their application for fault diagnosis. As seen in Table 4, most existing systems focus primarily on continuous DAQ for general performance monitoring rather than the specialized requirements of fault diagnosis, which often demands high-resolution, module-level data. Conventional DAQ systems typically lack the granularity required for isolating specific fault types, such as partial shading, degradation, or hot spots, which can significantly impact the efficiency and longevity of PV systems. For instance, while many systems include irradiance and temperature sensors, the absence of detailed current and voltage measurements at the module level limits their ability to detect and localize certain faults promptly.

Furthermore, the communication protocols in existing DAQ systems may not support the real-time responsiveness required for fault diagnosis in large-scale PV systems. For example, the reliance on low-bandwidth wireless protocols, such as ZigBee or LoRa, can delay fault detection and compromise the efficacy of corrective actions in extensive arrays. High-frequency data transmission, which is essential for capturing transient faults, is often constrained by these networks, impacting the effectiveness of diagnosis processes in time-sensitive scenarios.

Software limitations also hinder the diagnosis potential of conventional DAQ systems. Although platforms like LABVIEW and MATLAB allow for extensive customization, the integration of advanced fault-detection algorithms within these systems can be complex and may require significant computational resources. Additionally, the storage solutions employed by existing DAQ systems may not accommodate the large volumes of data generated by high-resolution, diagnosis-focused monitoring, necessitating advanced data management techniques such

as adaptive sampling or data compression to maintain storage efficiency without sacrificing diagnosis accuracy.

Conventional DAQ systems provide valuable infrastructure for basic monitoring in PV systems; however, their limited support for high-resolution, fault-specific DAQ and real-time diagnosis introduces challenges for comprehensive fault detection. Enhancements in sensor granularity, data transmission capacity, and data processing capabilities are necessary to support more robust diagnosis applications in PV systems.

### 3.5. Recommended instrumentation by fault type

Prior to outlining specific instrumentation recommendations by fault type, it is worthwhile to emphasize that a number of peer-reviewed studies have acknowledged that inadequate DAQ quality directly undermines the efficacy of photovoltaic fault diagnosis.

Tchio et al. demonstrated that misconfigured or low-accuracy sensors can lead to misdiagnosis of common faults such as open-circuit conditions and partial shading, emphasizing the importance of precision and sampling rate in DAQ systems [106]. Koumpli further highlighted that the lack of synchronized and reliable meteorological and electrical data impairs the proper evaluation of PV system performance, especially in residential-scale installations [107]. Similarly, Compaore et al. showed that poor data quality complicates the implementation of neural networks for PV fault classification and results in lower diagnostic accuracy [108].

Recent research has emphasized that DAQ system quality plays a critical role in the development of fault diagnosis algorithms for PV systems. One study provides a comprehensive bibliometric analysis, highlighting that robust, high-frequency, and context-aware datasets are essential for effective Artificial intelligence (AI)-driven diagnostics [5]. It identifies key requirements such as appropriate sampling rates, data domain fusion (e.g., thermal and electrical), and normalization procedures that adapt input data across heterogeneous PV installations. In a systematic review, Mansouri et al. emphasized that deep learning models for PV fault detection are strongly affected by the quality of input data, noting that noisy or inconsistent measurements significantly reduce the effectiveness of predictive maintenance tools [109].

Another investigation demonstrates that a cascaded feature extraction approach using wavelet transforms and commercial optimizers (e.g., Tigo) can successfully predict PV system health using PLS regression, though its performance is limited by typical 15 min acquisition intervals [12]. Theocharides et al. quantified the impact of data quality on solar forecasting models, finding that low-quality training data increases day-ahead prediction error by over 3%, directly affecting both fault detection and performance estimation [110]. Additionally, a fault detection framework based on ensemble learning for snail trail identification confirms that both the length of temporal slices and the presence of parasitic noise in electrical signals significantly influence diagnostic reliability. That work also proposes strategies such as signal normalization, database expansion, and hardware integration as future directions to overcome current acquisition limitations [24].
<table><caption>**Table 4**
Conventional PV data acquisition systems developed over time (Last 26 years).</caption><thead><tr><th>Year</th><th>Ref</th><th>Applications</th><th>Measurements</th><th>Platform</th><th>Software</th><th>ADC resolution</th><th>Sampling interval</th><th>Data transmission</th><th>Data storage</th></tr></thead><tbody><tr><td>1998</td><td>[78]</td><td>Solar radiation</td><td>Irradiance</td><td>ST62E20</td><td>Turbo C++</td><td>8-bit</td><td>10 min</td><td>Wired RS232</td><td>EEPROM</td></tr><tr><td>1998</td><td>[81]</td><td>PV plant</td><td>Irradiance, ambient temperature, PV module voltage and currents, battery voltage, battery charge current, load current</td><td>Processor MC 68B09</td><td>—</td><td>—</td><td>—</td><td>Wired RS232C</td><td>—</td></tr><tr><td>2001</td><td>[82]</td><td>Residential (RES) System</td><td>Ambient temperature, irradiance, voltage, current and power</td><td>DataTaker DT50</td><td>Labview</td><td>—</td><td>5 min</td><td>Wired RS232</td><td>PC</td></tr><tr><td>2003</td><td>[83]</td><td>RES System</td><td>Ambient temperature, irradiance, voltage, current and power</td><td>PCI-6024E</td><td>Labview</td><td>12-bit</td><td>1 min</td><td>Wired RS232</td><td>PC</td></tr><tr><td>2005</td><td>[84]</td><td>RES System</td><td>Ambient temperature, irradiance, wind, humidity, voltage, current and power</td><td>Commercial DAQ unit</td><td>VB, SQL Server 2000</td><td>—</td><td>1 min</td><td>RF</td><td>PC</td></tr><tr><td>2005</td><td>[85]</td><td>Grid connected PV plant</td><td>DC current, DC voltage, AC current, AC voltage, energy, power, ambient temperature, solar radiation, IeV curve</td><td>FP DAQ board</td><td>Labview</td><td>16-bit</td><td>30 s</td><td>Wired RS232</td><td>PC</td></tr><tr><td>2008</td><td>[86]</td><td>RES System</td><td>Wind speed, the panel positions of the solar module, the currents and the voltages of the solar and the wind systems</td><td>PIC 18F452 and PIC 18F2550</td><td>C#</td><td>10-bit</td><td>—</td><td>Wired RS485</td><td>PC</td></tr><tr><td>2011</td><td>[87]</td><td>PV LED Street Lighting</td><td>Daily energy, total energy used, charging or discharging status and fault condition</td><td>Atmel Atmega8</td><td>—</td><td>—</td><td>—</td><td>—</td><td>EEPROM</td></tr><tr><td>2011</td><td>[88]</td><td>Small PV installation</td><td>PV array voltage, PV array current, ambient temperature, solar irradiance</td><td>PIC16F877a Microcontroller</td><td>Labview</td><td>8-bit</td><td>—</td><td>Wired RS232</td><td>—</td></tr><tr><td>2012</td><td>[89]</td><td>PV Water Pump</td><td>Water flow rate, ambient temperature, global irradiation (inclined and horizontal), voltages, currents</td><td>HOBO</td><td>HOBO</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>2012</td><td>[90]</td><td>PV module temperature</td><td>PV module temperature</td><td>PC</td><td>Qbasic routine on PC</td><td>12-bit</td><td>1 min</td><td>Wired RS232</td><td>PC</td></tr><tr><td>2012</td><td>[91]</td><td>Grid connected PV plant</td><td>Solar radiation, wind speed, voltage and current of PV modules</td><td>Digital Signal Processor (DSP)</td><td>Labview, C</td><td>16-bit</td><td>—</td><td>ZigBee</td><td>PC</td></tr><tr><td>2014</td><td>[92]</td><td>Stand-Alone Photovoltaic (SAPV) system (fridge application)</td><td>Currents, voltages, temperatures of the fridge (internal and external), temperatures of PV plant, ambient temperature, humidity, irradiance</td><td>ET-7017 produced by ICPDAS</td><td>Matlab</td><td>—</td><td>—</td><td>Wired (Modbus Protocol)</td><td>Database</td></tr><tr><td>2015</td><td>[93]</td><td>Weather station</td><td>Humidity, atmospheric temperature, wind speed, wind direction, rainfall, solar radiation, surface temperature, ambient temperature, atmospheric pressure</td><td>PIC16F887 Microcontroller</td><td>Weather Monitoring Station (Embedded C Language)</td><td>14-bit</td><td>30 s</td><td>ZigBee</td><td>MySQL server</td></tr></tbody></table>

(continued on next page)
<table><caption>Table 4 (continued).</caption><tr><td>2015</td><td>[94]</td><td>Grid connected PV plant</td><td>Solar radiation, ambient temperature, module temperature, PV voltage, PV current, grid voltage and grid current</td><td>PIC18F8720</td><td>C/Netbeans</td><td>12-bit</td><td>1 min</td><td>ZigBee</td><td>EEPROM</td></tr><tr><td>2017</td><td>[95]</td><td>Environment parameters</td><td>Temperature, humidity, wind</td><td>LP3500</td><td>Dynamic C</td><td>–</td><td>1 min</td><td>GSM</td><td>SD</td></tr><tr><td>2017</td><td>[96]</td><td>Fault diagnosis</td><td>Temperatures, irradiance, voltages, currents and curves I-V/P-V</td><td>PIC18F8720</td><td>Solar Pro Software</td><td>12-bit</td><td>–</td><td>ZigBee</td><td>PC</td></tr><tr><td>2017</td><td>[97]</td><td>SAPV system (fridge application)</td><td>Temperatures, irradiance, voltages, currents and curves I-V/P-V</td><td>DAQ board NI USB-6009</td><td>Labview</td><td>12-bit</td><td>–</td><td>–</td><td>–</td></tr><tr><td>2018</td><td>[98]</td><td>RES System</td><td>Voltage, current, temperature, humidity</td><td>Arduino Mega</td><td>Embedded C</td><td>10-bit</td><td>10 min</td><td>SD Card</td><td>CSV File</td></tr><tr><td>2019</td><td>[99]</td><td>Remote PV monitoring</td><td>Voltage, temperature, current</td><td>IoT-based system</td><td>Custom IoT platform</td><td>–</td><td>Real-time</td><td>Wi-Fi</td><td>Cloud-based</td></tr><tr><td>2019</td><td>[100]</td><td>Meteorological and PV module monitoring</td><td>Temperature, irradiation</td><td>IoT and sensor network</td><td>Embedded software</td><td>12-bit</td><td>–</td><td>Wi-Fi</td><td>Local server</td></tr><tr><td>2020</td><td>[101]</td><td>Remote PV monitoring</td><td>Voltage, current, power, irradiance</td><td>IoT DAQ</td><td>Cloud-based</td><td>–</td><td>Real-time</td><td>Wi-Fi</td><td>Cloud</td></tr><tr><td>2021</td><td>[102]</td><td>Real-time PV monitoring</td><td>Voltage, temperature, power</td><td>IoT with Zigbee</td><td>Custom software</td><td>10-bit</td><td>15 min</td><td>Wi-Fi</td><td>Database</td></tr><tr><td>2022</td><td>[103]</td><td>PV plant monitoring</td><td>Voltage, current, irradiance</td><td>Open-source IoT</td><td>Linux, C++</td><td>12-bit</td><td>1 min</td><td>Wi-Fi</td><td>Database</td></tr><tr><td>2023</td><td>[104]</td><td>Standalone PV monitoring</td><td>Voltage, current, temperature, humidity</td><td>Custom IoT DAQ</td><td>Embedded C</td><td>10-bit</td><td>10 min</td><td>Wi-Fi</td><td>Server</td></tr><tr><td>2023</td><td>[105]</td><td>Solar power monitoring</td><td>Voltage, temperature, power</td><td>IoT-based system</td><td>Custom IoT app</td><td>10-bit</td><td>5 min</td><td>4G</td><td>Cloud</td></tr></table>
These findings further reinforce the need for a structured instrumentation framework — such as the one presented here — that defines clear requirements for sensor type, spatial coverage, and temporal resolution tailored to each fault category. For instance, Okere et al. demonstrated that module-level monitoring provides significantly better fault localization compared to string- or array-level approaches, particularly for detecting subtle or early-stage anomalies that are often missed by coarser monitoring strategies [111].

Similarly, in a technical review conducted by the Florida Solar Energy Center, it was concluded that relying solely on inverter- or SCADA-level data frequently results in insufficient diagnostic coverage, especially in large-scale photovoltaic systems where localized faults remain undetected until performance loss becomes severe [112].

Ansari et al. further supported these observations by analyzing various monitoring technologies and noting that lower spatial and temporal resolutions tend to limit the responsiveness and precision of diagnostic tools, especially in dynamic environmental conditions [113].

In addition, Betti et al. conducted a field-based predictive maintenance study and found that diagnostics based exclusively on inverter data and low sampling rates led to delayed identification of performance degradation and localized electrical imbalances [114].

Finally, Quiles-Cucarella et al. emphasized that the scarcity of labeled historical datasets represents a critical limitation for training robust machine learning models for PV fault detection, highlighting the dependence of algorithmic performance on data quality and resolution [115].

In light of these limitations and supporting evidence, a structured instrumentation framework becomes essential—not only to guide the selection of sensors and acquisition parameters, but also to ensure diagnostic accuracy across a wide range of PV fault scenarios.

Building upon these insights, the following section outlines instrumentation recommendations tailored to each fault type. In PV fault diagnosis, the effectiveness of detection strategies is strongly influenced by the type, quality, and configuration of DAQ systems employed. To enhance diagnostic performance while minimizing cost and complexity, Table 5 summarizes evidence-based recommendations for instrumentation and data specifications tailored to common PV fault types.

The fault types included in Table 5 reflect those for which peer-reviewed studies reported, either directly or implicitly, relevant sensor and DAQ strategies. All fault types included in Table 5 are supported by peer-reviewed literature. In most cases, the cited studies explicitly reported sensor types and DAQ parameters. In a few entries, however, not all specifications — such as temporal resolution or spatial granularity — were detailed in the original articles. In such cases, these parameters were logically inferred based on reported fault signatures and common engineering practice.

Each entry in Table 5 is grounded in published experimental or field-based validation. Partial shading was analyzed by Alajmi and Abdel-Qader [116] using current and voltage monitoring frameworks. Given the sensitivity of this fault type to irradiance mismatch, additional sensing of solar irradiance is recommended to improve detection reliability. Hotspot identification was demonstrated using infrared thermography in real-world conditions by Tajwar et al. who employed thermal imaging and object detection algorithms to locate overheating regions in PV modules [117]. Rocky et al. proposed a magnetic field-based diagnostic technique capable of detecting circuit-level anomalies such as cell degradation and local short circuits, validated through electroluminescence and temperature profiling [118].

PID was experimentally induced and assessed by Kwembur et al. using electroluminescence imaging and I-V characterization, which revealed substantial power losses and changes in resistance parameters under leakage current stress [119]. Sensor faults were diagnosed using ensemble learning models combined with Exponentially Weighted Moving Average (EWMA) control charts, as proposed by Harrou et al. who demonstrated the effectiveness of the approach on pyranometer

fault scenarios simulated under nominal PV system conditions [120]. Series arc faults were detected and localized using the high-frequency components of capacitor current transients, as demonstrated by Xiong et al. [121]. Inverter anomalies were characterized by Omana et al. who proposed early fault detection based on variations in harmonic content due to Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) on-state resistance degradation [122].

Connector faults were characterized by Miller et al. [123], who conducted mechanical and electrical assessments to identify degradation through increased contact resistance and structural wear under environmental cycling. Ground faults were addressed by Xie et al. [124], who proposed voltage-based protection techniques capable of detecting single-phase-to-ground faults in distribution networks with integrated photovoltaic systems. Delamination in photovoltaic modules was effectively detected and characterized through active infrared thermography, using different thermal excitation waveforms to optimize defect visibility, as demonstrated by Sinha and Gupta [125]. Dhimish and Holmes [126] presented an enhanced electroluminescence imaging method using binary and Fourier processing to detect microcracks in solar cells more accurately.

Corrosion effects were analyzed by Liggett et al. who identified increased contact resistance and thermal signatures using infrared imaging in field-exposed PV modules [127]. Maximum Power Point Tracking (MPPT)-related faults were addressed by Li et al. [128], who proposed a fault diagnosis method that exploits MPPT dynamics and inflection points in the I-V curve to detect anomalies rapidly and accurately under variable conditions.

Assessing how instrumentation recommendations have been adopted in recent experimental studies is critical to understanding their real-world applicability. Accordingly, we present a synthesis of peer-reviewed works published between 2020 and 2024 that report on validated PV fault diagnosis experiments. This synthesis compiles and compares sensor configurations, sampling rates, communication protocols, and reported diagnostic accuracies across diverse setups. The resulting comparative overview not only highlights best practices but also exposes persistent gaps—particularly in terms of data granularity, temporal resolution, and standardization. These findings further underscore the importance of implementing structured instrumentation frameworks such as the one proposed in this study.

### 3.5.1. Summary of experimentally validated diagnostic studies (2020–2024)

To provide a concise synthesis of current diagnostic practices, Table 6 summarizes a selection of experimentally validated fault diagnosis studies published between 2020 and 2024. The year 2025 was excluded from the table because the year is ongoing, and no published experimental studies meeting all instrumentation-reporting criteria were identified at the time of writing.

Importantly, while a substantial number of articles propose new PV fault diagnosis techniques, very few provide detailed information about the associated instrumentation—such as sensor configuration, sampling rate, and communication protocols. This limited transparency hinders reproducibility and complicates the evaluation of diagnostic reliability. To illustrate this gap and emphasize the relevance of standardized instrumentation strategies, Table 6 compiles a subset of studies from the last five years that do report these details. Though limited in number, these examples demonstrate a wide variation in instrumentation practices and reinforce the need for structured frameworks such as the one proposed in this work.

Each entry in the table includes the sensor suite, sampling rate, communication protocol, system scale, diagnostic approach, and reported classification accuracy. The resulting synthesis identifies common limitations (e.g., coarse sampling, vague reporting), as well as emerging best practices in the field of PV fault detection.

It is important to note that the diagnostic accuracies reported in Table 6 should be interpreted with caution. While the table highlights
<table><caption>**Table 5**
Recommended monitoring strategies by photovoltaic fault type.</caption><thead><tr><th>Fault type</th><th>Key measurable symptoms</th><th>Essential variables</th><th>Suggested sensors</th><th>Spatial resolution</th><th>Temporal resolution</th><th>Source</th></tr></thead><tbody><tr><td>Partial Shading</td><td>Current mismatch, power drop</td><td>Irradiance, string current</td><td>Pyranometer, current sensor</td><td>Module-level</td><td>≤10 s</td><td>[116]</td></tr><tr><td>Hotspot</td><td>Localized heating, module discoloration</td><td>Surface temperature</td><td>IR camera, thermocouples</td><td>Cell/module-level</td><td>1–5 min</td><td>[117]</td></tr><tr><td>Busbar/Circuit fault</td><td>Magnetic field asymmetry, temperature anomalies</td><td>Magnetic flux, surface temp</td><td>Integrated magnetic sensors, IR camera</td><td>Module-level</td><td>1–5 min</td><td>[118]</td></tr><tr><td>PID</td><td>Power drop, shunt resistance reduction</td><td>Leakage current, I–V characteristics</td><td>Electroluminescence imaging, I–V tracer</td><td>Module-level</td><td>Daily</td><td>[119]</td></tr><tr><td>Sensor fault</td><td>Implausible or drifting readings</td><td>Redundancy metrics</td><td>Dual sensors, ensemble models</td><td>Sensor-level</td><td>Continuous</td><td>[120]</td></tr><tr><td>Arc fault</td><td>High-frequency noise, overheating</td><td>Capacitor current transients</td><td>High-bandwidth current probe, capacitor</td><td>String-level</td><td>&lt;1 s</td><td>[121]</td></tr><tr><td>Inverter fault</td><td>MOSFET degradation, harmonic distortion</td><td>Output current spectrum, R<sub>on</sub></td><td>Harmonic analyzer, microcontroller monitor</td><td>Inverter-level</td><td>1–5 s</td><td>[122]</td></tr><tr><td>Connector fault</td><td>Contact resistance rise, mechanical degradation</td><td>Contact resistance, physical stress markers</td><td>Micro-ohmmeter, mechanical inspection</td><td>Connector-level</td><td>Periodic</td><td>[123]</td></tr><tr><td>Ground fault</td><td>Voltage unbalance, fault currents</td><td>Line voltage, zero-sequence voltage</td><td>Voltage sensors, relay monitor</td><td>Array-level</td><td>≤1 s</td><td>[124]</td></tr><tr><td>Delamination</td><td>Reflection loss, hotspot potential</td><td>IR image contrast</td><td>IR thermography system</td><td>Module-level</td><td>Daily–weekly</td><td>[125]</td></tr><tr><td>Microcracks</td><td>Output power loss, increased degradation</td><td>EL imaging pattern enhancement</td><td>Electroluminescence camera</td><td>Module-level</td><td>Periodic</td><td>[126]</td></tr><tr><td>Corrosion</td><td>Contact resistance increase</td><td>Voltage drop, IR image</td><td>IR camera, contact probes</td><td>Connector-level</td><td>Weekly</td><td>[127]</td></tr><tr><td>MPPT fault</td><td>MPP deviation, mismatch, tracking anomalies</td><td>DC voltage, current, power slope</td><td>MPPT logger, waveform tracer</td><td>Inverter-level</td><td>≤1 s</td><td>[128]</td></tr><tr><td>Bypass diode failure</td><td>Localized heating, power loss under shading</td><td>Reverse current, diode voltage drop</td><td>IR camera, I–V tracer</td><td>Module-level</td><td>1–5 min</td><td>[129]</td></tr><tr><td>Snail trail</td><td>Surface discoloration, degradation pattern</td><td>Surface anomaly pattern, thermal correlation, current deviation</td><td>Visual inspection, IR imaging, ensemble model</td><td>Module-level</td><td>Daily–weekly</td><td>[24]</td></tr><tr><td>Inverter Trip</td><td>Output interruption, status event triggers</td><td>Inverter state, AC/DC voltage</td><td>Inverter logger, event monitor</td><td>Inverter-level</td><td>Event-based</td><td>[122]</td></tr></tbody></table>

instrumentation setups, these performance figures are also strongly influenced by the diagnostic algorithms employed—ranging from simple thresholding to complex machine learning ensembles. As such, high or low accuracy values may reflect not only the sensor configuration and data quality but also the inherent strengths or limitations of the chosen fault detection method. Consequently, this synthesis should be viewed as a combined representation of both instrumentation and algorithmic design rather than a direct benchmark of monitoring effectiveness alone.

## 3.6. Instrumentation requirements according to IEC 61724-1

The IEC standard 61724-1:2021 establishes guidelines for the monitoring of PV system performance. These guidelines ensure that measured data is accurate, reliable, and comparable across installations. The standard classifies monitoring systems based on the accuracy of their instrumentation and provides detailed requirements for sensor performance, installation practices, and calibration procedures.

In the context of fault detection and system diagnostics, adhering to IEC 61724-1 ensures that measurements of irradiance, temperature, and electrical output are not only technically sound but also compliant with globally recognized criteria. This section summarizes the key aspects of the standard relevant to sensor classification, proper mounting, and calibration protocols, all of which are critical for consistent and high-quality PV performance monitoring.

### 3.6.1. Sensor classification

Sensors for PV system monitoring are classified into accuracy classes defined by the IEC 61724-1:2021 standard to ensure data reliability across different system types and applications [7].

Two sensor accuracy classes are specified:

* **Class A (High Accuracy):** Intended for large-scale utility and commercial PV systems, requiring the highest measurement precision and lowest uncertainty.

* **Class B (Medium Accuracy):** Appropriate for smaller or less critical systems such as residential and small commercial installations.

* **Class C:** This class has been deprecated as it did not meet the minimum accuracy and traceability requirements established in the 2021 revision.

For irradiance measurements, Class A systems must employ spectrally flat pyranometers that conform to Class A of International Organization for Standardization (ISO) 9060:2018, with calibration uncertainty ≤2% at 1000 W/m², a measurement range up to 1500 W/m², and resolution ≤1 W/m². Class B systems may use pyranometers or PV reference devices classified as Class C or better under ISO 9060:2018, with slightly relaxed calibration uncertainty of ≤3% [7, Section 8.2.2, Table 4].

Reference PV devices used in both Class A and Class B systems must comply with IEC 60904-2, with Class A requiring calibration uncertainty ≤2% and Class B allowing up to 3% [7, Table 4].

Sampling and recording intervals also differ by sensor class. For Class A, the maximum sampling interval is 5 s, and the recording
<table><caption>**Table 6**
Summary of validated PV fault diagnosis studies (2020–2024).</caption><thead><tr><th>Study</th><th>Sensor suite</th><th>Sampling rate</th><th>Comms protocol</th><th>System scale</th><th>Diagnostic method</th><th>Accuracy (%)</th><th>Samples</th></tr></thead><tbody><tr><td>2024, [130]</td><td>Pyranometer (Kipp & Zonen CM11), K-type thermocouples, Data logger (Agilent 34970)</td><td>1 min</td><td>Wired (Data logger)</td><td>PV plant (9.54 kW, 3x sub-arrays)</td><td>Fault detection via model parameter extraction</td><td>99.4%</td><td>437,290</td></tr><tr><td>2024, [131]</td><td>Thermal camera (DJI Mavic 3 Thermal)</td><td>Static imaging</td><td>RTK/GNSS + Wi-Fi (drone)</td><td>PV modules (lab+field)</td><td>Image-based classification</td><td>N/A</td><td>1000</td></tr><tr><td>2024, [24]</td><td>Tigo platform (V, I, irradiance, temperature)</td><td>15 min</td><td>Wi-Fi</td><td>PV plant</td><td>Ensemble Learning (RF + SVM + KNN)</td><td>89.0</td><td>16</td></tr><tr><td>2023, [132]</td><td>Voltmeter, Ammeter, IR thermometer, Solar power meter</td><td>15 min</td><td>Offline (manual)</td><td>PV array (3 x 3)</td><td>Fault signature analysis (P-V curves)</td><td>N/A</td><td>9</td></tr><tr><td>2023, [133]</td><td>Voltage, current, temperature, irradiance, humidity sensors</td><td>1 s</td><td>ZigBee, Wi-Fi, LoRa</td><td>PV panel</td><td>Feed-Forward ANN</td><td>N/A</td><td>N/A</td></tr><tr><td>2022, [12]</td><td>Tigo platform (V, I, irradiance, temperature)</td><td>15 min</td><td>Wi-Fi</td><td>PV plant</td><td>PLS-LDA (Partial Least Squares + Linear Discriminant Analysis)</td><td>87.5</td><td>12</td></tr><tr><td>2021, [134]</td><td>Pyranometer (Apogee PYR-S), Temperature (DS18B20), Current (INA226), Humidity (DHT22)</td><td>N/A</td><td>Wi-Fi (HTTP REST POST to cloud)</td><td>Residential PV system</td><td>Decision Tree (J48), SVM, ANN (comparative study)</td><td>96.3</td><td>76,728</td></tr><tr><td>2020, [24]</td><td>Pyranometer, PT100 sensors, NI 9215 module, current transducer</td><td>1 ms/1 s</td><td>Modbus TCP/IP</td><td>PV plant</td><td>Ensemble (k-NN + SVM + RF)</td><td>76-84</td><td>16</td></tr><tr><td>2020, [135]</td><td>Weather station sensors, electrical sensors (voltage, current)</td><td>1 min</td><td>N/A</td><td>Rooftop PV (0.9 kWp)</td><td>SDGM with SVM, k-NN benchmarks</td><td>92.5</td><td>15,000</td></tr><tr><td>2020, [136]</td><td>Pyranometer (EKO MS-40), PT100, NI 9215/9242, HASS-50S</td><td>1 s</td><td>Modbus TCP/IP</td><td>Grid-connected PV</td><td>Artificial Neural Network (ANN)</td><td>95.44</td><td>5054</td></tr></tbody></table>

interval is 5 min (with 1 min recommended), while Class B systems may sample every 1 min and record every 15 minutes [7, Section 6.1, Table 1].

These classifications dictate not only sensor precision but also maintenance expectations, recalibration frequency, and DAQ rates, ensuring harmonization in monitoring practices and enabling performance benchmarking across PV installations. Beyond sensor selection, correct installation is essential to ensure valid measurements.

### 3.6.2. Sensor mounting

Proper installation practices are essential for ensuring reliable and representative measurements in photovoltaic monitoring systems, as specified by IEC 61724-1:2021 [7].

#### A. Irradiance sensors.

Front-side irradiance sensors, including Global Horizontal Irradiance and Plane-of-Array sensors, must be mounted at the same tilt and azimuth angles as the PV modules. This alignment can be achieved by affixing sensors directly onto the racking or on separate mounts with equivalent orientation. These sensors must be positioned to avoid shading during the entire solar day, except within approximately 30 min of sunrise or sunset. If unavoidable, such shading events must be documented. Rear-side irradiance sensors must be installed with a representative field of view of rear-side conditions, preferably distributed across the racking to capture non-uniform illumination. All irradiance sensors should be shielded from reflections and obstructions [7, Section 8.2.3].

#### B. Albedo sensors.

Horizontal albedo sensors should be mounted at a minimum height of 1.0 m to ensure a wide, unobstructed field of view of the ground-reflected irradiance. These should be free from shadowing by vegetation, modules, or structural elements within ±80° of their field of view [7, Section 8.2.3.3].

#### C. Temperature sensors.

Module temperature sensors must be placed on the back sheet of a centrally located and representative PV cell. For crystalline silicon modules, the central cell is preferred. For thin-film modules, sensors should be installed within a single cell and away from inter-cell scribe lines. Sensors must be attached with minimal air gaps and thermal resistance.

Two attachment methods are described:

* **Permanent attachment:** The sensor is bonded to the module with thermally conductive epoxy, covered by polyester tape for long-term stability. This method is suited for long-term monitoring and is illustrated in Fig. 13.

* **Temporary attachment:** The sensor is affixed using a silicone-adhesive polyester tape, allowing for repositioning. Suitable for short-term measurements such as during commissioning (see Fig. 14).

The use of cyanoacrylate adhesives is discouraged due to their potential to degrade common back sheet materials such as Polyethylene Terephthalate (PET) and Polytetrafluoroethylene (PTFE) [7, Annex B]. Additionally, strain relief of sensor wiring must be provided using polyester tape to prevent tension on the sensing element (Fig. 15).

Even with accurate sensors and proper installation, routine calibration is necessary to maintain long-term reliability.

### 3.6.3. Calibration and traceability

To ensure accurate and consistent measurements, the IEC 61724-1:2021 standard mandates specific calibration intervals and procedures for monitoring sensors [7, Section 8.2.4].

For Class A systems, sensors must be recalibrated at least once every two years, or more frequently if specified by the manufacturer. Acceptable calibration methods include reference procedures traceable to ISO 9847 or the World Radiometric Reference, ensuring international comparability. In Class B systems, recalibration frequency is determined by manufacturer guidance.
<figure><img src="image_15.png" /><figcaption>Fig. 13. Sensor attachment, permanent [7, Annex B].</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Fig. 14. Sensor attachment, temporary [7, Annex B].</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Fig. 15. Sensor element wire strain relief [7, Annex B].</figcaption></figure>

Sensor downtime must be minimized during recalibration activities. This can be achieved by:

* Swapping sensors with pre-calibrated replacements,

* Conducting on-site recalibration procedures when applicable,

* Installing redundant sensors and alternating their laboratory calibration schedules.

All sensors must have traceable metadata documented and maintained throughout their operational life. The following details shall be logged:

* Date of calibration,

* Calibration method and standard used (e.g., ISO 9847),

* Sensor class designation (A, B, or C),

* Manufacturer and model,

* Serial number or unique identifier.

Maintaining comprehensive calibration records is essential for auditability, quality assurance, and long-term performance evaluation of PV monitoring systems.

These requirements are essential to ensure comparability across monitoring systems, and deviations can significantly affect performance analyses and fault diagnostics. By supporting long-term performance evaluation and enabling accurate fault detection, they ensure the integrity of PV monitoring.

## 4. Discussion

This study establishes a structured framework for instrumentation in PV systems, detailing criteria for sensor selection, adaptive communication, and data processing protocols necessary to achieve scalable, precise diagnosis. Although the framework is comprehensive, several critical areas present both challenges and opportunities for further refinement in PV monitoring.

One of the primary strengths of the framework is its focus on sensor deployment strategies. By emphasizing PV topology and system scale, as highlighted in Section 3.1, the framework attempts to optimize diagnosis resolution while balancing cost-efficiency. However, achieving this balance is not without challenges. Module-level monitoring enables early detection of localized issues, such as shading and degradation, but it involves significant trade-offs in terms of installation and maintenance complexity. For large installations, where local faults can escalate unnoticed, the practicalities of deploying extensive module-level sensors may be prohibitive. Thus, the scalability of such high-granularity monitoring requires further exploration, particularly in identifying optimal points of diminishing returns where additional sensors do not significantly improve fault detection.

In terms of DAQ, the framework offers practical recommendations for selecting sampling rates and communication protocols; however, these guidelines bring into focus some key limitations of current PV systems. As explored in Section 3.3, wired protocols like Ethernet provide the high-speed, low-latency capabilities necessary for large-scale systems, while wireless options such as ZigBee and LoRa offer advantages for remote installations. Yet, each approach poses specific challenges. Wired protocols, while stable, are costly and less flexible in adapting to expanding or shifting system configurations, which limits their applicability for dynamic PV installations. Conversely, wireless protocols, although scalable, face issues with latency and interference, which can be critical for real-time fault diagnosis. This indicates a need for continued research into hybrid communication models that can integrate the strengths of both wired and wireless protocols to meet diverse PV monitoring requirements effectively.

The framework also highlights the importance of advanced preprocessing and adaptive sampling in managing the massive data volumes generated by PV systems, as outlined in Section 3.4.3. Adaptive sampling, which dynamically adjusts the sampling rate based on operational needs, is particularly valuable for conserving resources while preserving diagnosis accuracy. However, this approach introduces new technical and logistical challenges. For instance, determining the optimal thresholds for rate adjustments requires real-time, context-sensitive algorithms that can discriminate between routine variations and fault indicators. Without precise tuning, there is a risk of either undersampling critical events or overburdening storage resources with redundant data. Adaptive sampling offers a promising solution, but its effectiveness depends heavily on the sophistication of the underlying algorithms and processing capabilities, which may not be readily available in all PV systems.

In evaluating existing DAQ systems in Section 3.4.4, the study reveals substantial gaps between the current monitoring platforms and the demands of fault-specific diagnosis. Many conventional DAQ systems are designed primarily for operational monitoring rather than fault diagnosis, resulting in limitations in sensor granularity and real-time responsiveness. Specifically, the inability of these systems to capture high-resolution, module-level data restricts their utility in detecting transient faults or subtle performance deviations. Moreover, the reliance on low-bandwidth communication protocols in many DAQ systems limits their capacity for continuous, high-frequency data transmission, which is essential for accurate fault localization. This gap between monitoring capabilities and diagnosis needs indicates a pressing requirement for modular, scalable DAQ systems that can be customized for enhanced fault detection without incurring unsustainable costs.
The proposed framework offers substantial improvements over traditional DAQ systems; however, its practical implementation may be constrained by factors such as cost, system compatibility, and the need for specialized expertise in configuring adaptive data management. Consequently, this study highlights not only the advancements but also the current limitations of PV diagnosis instrumentation. The pathway to fully realizing the potential of the framework will likely require iterative improvements in hardware affordability, software adaptability, and the integration of machine learning algorithms for real-time fault prediction.

This framework represents a critical step toward robust PV system diagnosis, yet it also reveals the complexity and resource intensity involved in achieving scalable, real-time monitoring adaptable across diverse installations. Continued innovation in data processing techniques, communication infrastructure, and adaptive diagnosis is essential to bridge the gap between current PV monitoring practices and the advanced requirements of fault diagnosis in large-scale PV deployments.

This study also outlines a set of challenges associated with achieving effective instrumentation tailored for diagnostics, discusses the limitations of the current work, and highlights directions for future work.

## 4.1. Challenges and key aspects

As PV systems continue to evolve, the development of advanced instrumentation frameworks faces several challenges that must be addressed to enhance fault diagnosis and system reliability. These challenges encompass technical, operational, and security-related aspects.

One of the foremost challenges lies in the integration of advanced algorithms for data preprocessing and analysis. Modern PV monitoring systems must be equipped to handle high-frequency data streams efficiently, enabling the application of machine learning and artificial intelligence-based methods for fault classification and diagnosis. These approaches have been shown to enhance the detection of subtle or transient faults under varying environmental conditions [75].

The need for rapid cloud communication is another critical consideration. Advanced instrumentation systems must be capable of transmitting data to cloud-based platforms for remote processing and quickly retrieving diagnostic results. This capability enables real-time fault detection and predictive maintenance, ensuring optimal system performance [137]. For large-scale installations, cloud-connected digital twins have been proposed as an effective solution to monitor the health of PV systems and simulate potential fault scenarios [137].

Cybersecurity is an essential aspect of modern PV instrumentation. The increasing reliance on internet-connected devices exposes PV systems to potential cyber threats, necessitating secure communication protocols and data encryption. Blockchain technology and secure data transfer methods have been identified as effective tools to enhance cybersecurity in PV systems [138]. Incorporating these measures into the design of instrumentation frameworks will safeguard data integrity and system operations.

Sensor redundancy is another key area of focus, as it significantly enhances the robustness of fault diagnosis. The deployment of redundant sensors ensures continued monitoring and diagnostic capabilities even in the event of individual sensor failures. Distributed instrumentation systems, such as those based on agent-based modeling, can further improve system resilience by decentralizing data collection and fault diagnosis [139].

The optimization of sensor placement and quantity remains a critical challenge. Determining the optimal number and location of sensors requires careful consideration of the topology, scale, and specific fault detection requirements of the PV system. Advanced fault localization techniques, which adapt dynamically to operational conditions, can guide efficient sensor deployment strategies [140].

Minimizing system downtime during fault diagnosis is another important consideration. Instrumentation frameworks should be designed to facilitate data collection and transmission without interrupting the power generation process. The use of wireless sensor networks, coupled with low-power communication protocols such as LoRa, allows for seamless DAQ without impacting operational efficiency [137].

Finally, the development of smart and distributed instrumentation frameworks presents an opportunity to improve scalability and adaptability. By incorporating agent-based models, such systems can dynamically reconfigure themselves to accommodate changes in PV system operations or environmental conditions. This distributed approach has the potential to enhance fault detection sensitivity and system robustness [14].

## 4.2. Limitations

Despite the contributions of this study, some limitations must be acknowledged, which may influence the practical application and generalizability of the proposed framework. First, while this framework provides theoretical guidelines for sensor placement, DAQ, and processing, it does not include an empirical validation through implementation in operational PV systems. Therefore, the feasibility of the framework under real-world conditions, including environmental variability and installation-specific constraints, remains untested. Future studies are necessary to apply and validate these recommendations across a variety of PV configurations to assess their effectiveness in practice.

Another limitation is the generalization of the framework across different PV configurations and scales. Although designed to be adaptable, the framework may require further customization to address specific needs in different settings. For example, the sensor density and placement strategies proposed for utility-scale PV plants may not translate effectively to residential or small commercial installations, where cost constraints limit the use of extensive monitoring equipment. Moreover, variations in climate and environmental conditions — such as extreme temperatures or high dust accumulation — may necessitate specific adaptations in sensor types, placement, and data processing protocols.

In addition, while the framework proposes flexible communication protocols that include both wired and wireless options, the inherent limitations of wireless networks like ZigBee and LoRa in supporting high-frequency, low-latency data transfer could hinder real-time fault diagnosis, particularly for transient fault events. Wireless protocols, while scalable and cost-effective for large or remote PV fields, may fall short in installations that require immediate fault detection and response. In such cases, the need for hybrid networks that can support both high-bandwidth and low-latency requirements becomes apparent, but implementing such systems may introduce additional costs and complexity.

Advanced preprocessing techniques, such as machine learning-based anomaly detection, while effective in ensuring high data quality, can be computationally intensive and may not be feasible for smaller PV systems or installations with limited processing power. Such techniques are often resource-demanding and may require access to cloud-based storage and processing infrastructure, which could introduce latency issues and data privacy concerns. Thus, while the framework advocates for sophisticated preprocessing methods, practical implementation will need to balance these techniques with the computational capacity and resources available in different PV settings.

## 4.3. Future work

Future work can extend this study by conducting field trials that implement and assess the proposed instrumentation framework across a range of PV systems. Field experiments would offer valuable insights into the framework performance under real operational conditions, validating its diagnosis effectiveness and adaptability across diverse
environmental contexts and installation scales. For instance, applying the framework in both desert environments, where high dust levels could impact sensor reliability, and in temperate climates, where temperature fluctuations affect module performance, could reveal the extent to which this framework meets practical diagnosis needs. Such trials would also contribute to understanding the economic implications of deploying comprehensive instrumentation frameworks, balancing enhanced diagnosis capabilities with installation and maintenance costs.

Another promising area for future work lies in refining communication networks to better serve the needs of fault diagnosis in large PV arrays. Research could explore hybrid communication networks that combine the robustness of wired protocols with the flexibility of wireless systems. Such networks could enable rapid data transmission while maintaining scalability and low operational costs. For instance, hybrid networks could use Ethernet for critical high-frequency data points and wireless protocols for less time-sensitive data. Furthermore, advances in 5G technology may offer new avenues for high-bandwidth, low-latency data transmission in PV monitoring systems, potentially overcoming the limitations of current wireless options like ZigBee and LoRa.

Further studies should explore the development of intelligent sampling strategies that go beyond adaptive sampling, incorporating contextual awareness to optimize data collection based on environmental and operational changes. Such strategies could employ predictive models that adjust sampling rates dynamically, anticipating faults based on historical trends and real-time data. Additionally, multi-tiered data storage solutions that separate high-priority diagnosis data from general operational data could improve data management efficiency, enabling faster access to critical information for fault diagnosis without compromising long-term storage requirements.

Expanding the proposed framework through empirical validation, advanced diagnosis techniques, refined communication protocols, and intelligent data management strategies presents significant opportunities for future research to advance the capabilities and scalability of PV monitoring systems. Such progress promises to improve fault detection accuracy, reduce maintenance costs, and support the reliable integration of solar energy into the broader energy grid, thereby contributing to the sustainable growth of solar energy infrastructure worldwide.

## 5. Conclusions

The comprehensive framework developed in this study for PV instrumentation marks a significant advance in addressing the diagnosis needs of PV systems, particularly in the context of fault detection and operational reliability. Rather than merely extending conventional DAQ systems, which are often limited to basic operational oversight, this framework critically reevaluates and redefines the instrumentation requirements necessary for detailed, fault-specific monitoring. This approach aims to address the gap in current PV monitoring practices, which often lack the precision and responsiveness required for high-resolution diagnosis, especially at the module level (see Section 3.4.4).

This framework moves beyond existing standards and surveys by offering a fault-centric perspective that integrates instrumentation granularity, adaptive sampling, and heterogeneous data handling into a cohesive system. While standards typically present static guidelines, this study emphasizes dynamic operational needs and fault specificity, thus providing a more flexible and diagnostic-effective instrumentation architecture.

A notable strength of this study lies in its structured methodology for integrating system design, DAQ, and data processing into a unified framework (see Section 3). The proposed guidelines for sensor placement, communication protocols, and data handling offer practical solutions to the challenges that PV systems face, especially in large-scale applications. By focusing on diagnosis sensitivity, the framework

underscores the limitations of existing DAQ systems and highlights the need for advanced instrumentation capable of capturing transient faults and localized issues. However, while these recommendations present a robust foundation, the implementation of such a system will likely encounter logistical and economic constraints, particularly in scaling up for extensive PV installations.

The findings reveal that addressing the limitations of existing PV monitoring platforms involves more than technological adjustments; it requires a shift in approach to PV instrumentation, prioritizing not only operational data but also diagnosis accuracy. The proposed framework emphasizes the necessity of a responsive communication infrastructure and adaptive sampling rates that adjust dynamically to operational changes, which are essential for identifying and localizing faults in real-time. However, achieving these goals will depend heavily on continued advancements in communication protocols and data management strategies, areas that this framework identifies as key targets for future development (see Sections 3.3 and 3.4).

Finally, the limitations and challenges highlighted in the Discussion section (see Section 4) underscore the complexity of achieving a truly scalable and adaptable PV monitoring solution. Practical constraints, including cost, compatibility across diverse PV setups, and the need for specialized technical expertise, present hurdles to full implementation. The success of future iterations of this framework will likely hinge on addressing these issues through advancements in hardware affordability, software flexibility, and the integration of intelligent data processing techniques, such as machine learning, to enable predictive diagnosis. Such improvements are essential to transitioning from a conceptually sound framework to an economically viable and operationally scalable solution.

## Acronyms

**AC** Alternating Current

**ADC** Analog-to-Digital converter

**AI** Artificial intelligence

**BL** Bridge-linked

**DAQ** Data Acquisition

**DC** Direct Current

**DIC** Digital Image Correlation

**DSP** Digital Signal Processor

**DTW** Dynamic Time Warping

**DWT** Discrete Wavelet Transform

**EEPROM** Electrically-Erasable Programmable Read-Only Memory

**EL** Electroluminescence

**EWMA** Exponentially Weighted Moving Average

**IEA** International Energy Agency

**IEC** International Electrotechnical Commission

**IoT** Internet of Things

**IR** Infrared

**ISO** International Organization for Standardization

**LDA** Linear Discriminant Analysis
**LED** Light-Emitting Diode

**MOSFET** Metal-Oxide-Semiconductor Field-Effect Transistor

**MPPT** Maximum Power Point Tracking

**PET** Polyethylene Terephthalate

**PID** Potential-Induced Degradation

**PL** Photoluminescence

**PLC** Power Line Communication

**PLS** Partial Least Squares

**PTFE** Polytetrafluoroethylene

**PV** Photovoltaic

**RES** Residential

**RUV** Resonance Ultrasonic Vibrations

**SAPV** Stand-Alone Photovoltaic

**SCADA** Supervisory Control and Data Acquisition

**SD** Secure Digital

**SP** Series-parallel

**SPR** Surface Plasmon Resonance

**SSA** Singular Spectrum Analysis

**TCT** Total-cross-tied

**WSNS** Wireless Sensor Networks

## CRediT authorship contribution statement

**Edgar Hernando Sepúlveda-Oviedo:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
**Bruno Estibals:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This project acknowledges the ADREAM project from the LAASCNRS laboratory for granting permission to use and disseminate the images utilized in this work.

## Data availability

No private datasets were generated or analyzed during the current study. All data can be retrieved from the mentioned search engines using the search protocol described in this study.

## References

[1] L. Hernández-Callejo, S. Gallardo-Saavedra, V. Alonso-Gómez, A review of photovoltaic systems: Design, operation and maintenance, Sol Energy 188 (2019) 426-440.

[2] SPE, Global Market Outlook for Solar Power 2024-2028, SolarPower Europe, 2024.

[3] E.H. Sepúlveda-Oviedo, Impact of environmental factors on photovoltaic system performance degradation, Energy Strat. Rev. 59 (2025) 101682.

[4] E.H. Sepúlveda-Oviedo, A review of operational factors affecting photovoltaic system performance, Energy Convers Manag.: X 26 (2025) 100942.

[5] E.H. Sepúlveda-Oviedo, L. Travé-Massuyès, A. Subias, M. Pavlov, C. Alonso, Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach, Heliyon 9 (11) (2023) e21491.

[6] H. Belmili, S.M. Ait Cheikh, M. Haddadi, C. Larbes, Design and development of a data acquisition system for photovoltaic modules characterization, Renew. Energy 35 (7) (2010) 1484-1492, Special Section: IST National Conference 2009.

[7] IEC, IEC 61724-1:2021 Photovoltaic System Performance - Part 1: Monitoring, International Standard IEC 61724, 2021.

[8] A. El Hammoui, S. Motahhir, A. Chalh, eg. Abdelaziz, A. Derouich, Low-cost virtual instrumentation of PV panel characteristics using Excel and Arduino in comparison with traditional instrumentation, Renew.: Wind. Water Sol. 5 (2018) 1-16.

[9] N.H. Alombah, A. Harrison, J.N. Mungwe, F.F.C. Vincelas, H.F. Bertrand, Trio-PV monitor: A smart IoT-based instrument for continuous and reliable monitoring of solar PV installations, Eng. Proc. 56 (1) (2023).

[10] S. Voutsinas, D. Karolidis, I. Voyiatzis, M. Samarakou, Development of a fault detection algorithm for photovoltaic systems, in: PCI 2021: 25th Pan-Hellenic Conference on Informatics, 2021, pp. 84-87.

[11] Y. Yagi, H. Kishi, R. Hagihara, T. Tanaka, S. Kozuma, T. Ishida, et al., Diagnostic technology and an expert system for photovoltaic systems using the learning method, Sol. Energy Mater. Sol. Cells 75 (3) (2003) 655-663, PVSEC 12, PART III.

[12] E.H. Sepúlveda-Oviedo, L. Travé-Massuyès, A. Subias, C. Alonso, M. Pavlov, Feature extraction and health status prediction in PV systems, Adv. Eng. Inf. 53 (2022) 101696.

[13] D. Manno, G. Cipriani, G. Ciulla, V. Di Dio, S. Guarino, V. Lo Brano, Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images, Energy Convers. Manag. 241 (2021) 114315.

[14] P. Jain, J. Poon, J.P. Singh, C. Spanos, S.R. Sanders, S.K. Panda, A digital twin approach for fault diagnosis in distributed photovoltaic systems, IEEE Trans. Power Electron. 35 (1) (2020) 940-956.

[15] M.H. Ali, A. Rabhi, A.E. Hajjaji, G.M. Tina, Real time fault detection in photovoltaic systems, Energy Procedia 111 (2017) 914-923, 8th International Conference on Sustainability in Energy and Buildings, SEB-16, 11-13 2016, Turin, Italy.

[16] Z. Wang, R.S. Balog, Arc fault and flash detection in photovoltaic systems using wavelet transform and support vector machines, in: 2016 IEEE 43rd Photovoltaic Specialists Conference, PVSC, 2016, pp. 3275-3280.

[17] V. Kongphet, A. Migan-Dubois, C. Delpha, D. Diallo, J.Y. Lechenadec, Photovoltaic fault detection and diagnosis: Which level of granularity for PV modeling? in: 2020 Prognostics and Health Management Conference, PHM-Besançon, 2020, pp. 180-186.

[18] M.F. Lüscher, J. Zgraggen, Y. Guo, A. Notaristefano, L. Goren Huber, Data scarcity in fault detection for solar tracking systems: the power of physics-informed artificial intelligence, in: PHM Society European Conference, 2024, pp. 1-8.

[19] P. Lin, Y. Lin, Z.C. Chen, L. Wu, L. Chen, S. Cheng, A density peak-based clustering approach for fault diagnosis of photovoltaic arrays, Int. J. Photoenergy 2017 (2017) 1-14.

[20] Y. Zhao, D. Li, T. Lu, Q. Lv, N. Gu, L. Shang, Collaborative fault detection for large-scale photovoltaic systems, IEEE Trans. Sustain. Energy 11 (4) (2020) 2745-2754.

[21] M. Dhimish, V. Holmes, Fault detection algorithm for grid-connected photovoltaic plants, Sol. Energy 137 (2016) 236-245.

[22] P.R. Satpathy, R. Sharma, Parametric indicators for partial shading and fault prediction in photovoltaic arrays with various interconnection topologies, Energy Convers. Manag. 219 (2020) 113018.

[23] F. Aziz, A. Ul Haq, S. Ahmad, Y. Mahmoud, M. Jalal, U. Ali, A novel convolutional neural network-based approach for fault classification in photovoltaic arrays, IEEE Access. 8 (2020) 41889-41904.
[24] E.H. Sepúlveda-Oviedo, L. Travé-Massuyès, A. Subias, M. Pavlov, C. Alonso, An ensemble learning framework for snail trail fault detection and diagnosis in photovoltaic modules, Eng. Appl. Artif. Intell. 137 (2024) 109068.

[25] C. Li, Y. Yang, F. Fan, L. Xia, P. Peng, Y. Wang, et al., A novel methodology for partial shading diagnosis using the electrical parameters of photovoltaic strings, IEEE J. Photovolt. 12 (4) (2022) 1027-1035.

[26] M. Dhimish, P. Mather, V. Holmes, Novel photovoltaic hot-spotting fault detection algorithm, IEEE Trans. Device Mater. Reliab. 19 (2) (2019) 378-386.

[27] Z. Zhang, M. Ma, H. Wang, H. Wang, W. Ma, X. Zhang, A fault diagnosis method for photovoltaic module current mismatch based on numerical analysis and statistics, Sol Energy 225 (2021) 221-236.

[28] N. Pourshahbaz, A. Eskandari, J. Milimonfared, M. Aghaei, Current-based fault detection of photovoltaic systems, in: 2023 14th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC, 2023, pp. 1-6.

[29] H. Ding, K. Ding, J. Zhang, Y. Wang, L. Gao, Y. Li, et al., Local outlier factor-based fault detection and evaluation of photovoltaic system, Sol Energy 164 (2018) 139-148.

[30] A. Mehmood, H.A. Sher, A.F. Murtaza, K. Al-Haddad, A diode-based fault detection, classification, and localization method for photovoltaic array, IEEE Trans. Instrum. Meas. 70 (2021) 1-12.

[31] I.S. Kim, Fault detection algorithm of the photovoltaic system using wavelet transform, in: India International Conference on Power Electronics 2010, IICPE2010, 2011, pp. 1-6.

[32] R. Bakshi-Jafarabadi, J. Sadeh, M. Popov, Maximum power point tracking injection method for islanding detection of grid-connected photovoltaic systems in microgrid, IEEE Trans. Power Deliv. 36 (1) (2021) 168-179.

[33] O.K. Segbefia, A.G. Imenes, T.O. Søtre, Moisture ingress in photovoltaic modules: A review, Sol Energy 224 (2021) 889-906.

[34] IEC, Photovoltaic System Performance Monitoring—Guidelines for Measurement, Data Exchange and Analysis, International Standard IEC 61724, 1998.

[35] W. Chine, A. Mellit, V. Lughi, A. Malek, G. Sulligoi, A.M. Pavan, A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks, J. Renew. Energy 90 (2016) 501-512.

[36] S.R. Madeti, S.N. Singh, Monitoring system for photovoltaic plants: A review, Renew. Sustain. Energy Rev. 67 (2017) 1180-1207.

[37] A.W. Kandeal, M.R. Elkadeem, A. Kumar Thakur, G.B. Abdelaziz, R. Sathya-murthy, A.Y. Kabeel, et al., Infrared thermography-based condition monitoring of solar photovoltaic systems: A mini review of recent advances, Sol Energy 223 (2021) 33-43.

[38] N. Kellil, A. Aissat, A. Mellit, Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions, Energy 263 (2023) 125902.

[39] S.S. Akay, O. Özcan, O. Özcan, Ömer Yetemen, Efficiency analysis of solar farms by UAV-based thermal monitoring, Eng. Sci. Technol. Int. J. 53 (2024) 101688.

[40] D.L. King, J.A. Kratochvil, M.A. Quintana, T.J. McMahon, Applications for infrared imaging equipment in photovoltaic cell, module, and system testing, in: Conference Record of the Twenty-Eighth IEEE Photovoltaic Specialists Conference, 2000, pp. 1487-1490.

[41] I. Berardone, J.L. Garcia, M. Paggi, Analysis of electroluminescence and infrared thermal images of monocrystalline silicon photovoltaic modules after 20 years of outdoor use in a solar vehicle, Sol Energy 173 (2018) 478-486.

[42] V.E. Puranik, R. Kumar, R. Gupta, Progress in module level quantitative electroluminescence imaging of crystalline silicon PV module: A review, Sol Energy 264 (2023) 111994.

[43] B. Doll, E. Wittmann, L. Lüer, J. Hepp, C. Buerhop, J. Hauch, et al., Aerial photoluminescence imaging of photovoltaic modules, Phys. Status Solidi (RRL) - Rapid Res. Lett. (2023) 17.

[44] B. Doll, T. Pickel, O. Schreer, C. Zetzmann, J. Hepp, J. Denz, et al., High-throughput, outdoor characterization of photovoltaic modules by moving electroluminescence measurements, Opt. Eng. 58 (2019) 1.

[45] L. Koester, S. Lindig, A. Louwen, A. Astigarraga, G. Manzolini, D. Moser, Review of photovoltaic module degradation, field inspection techniques and techno-economic assessment, Renew. Sustain. Energy Rev. 165 (2022) 112616.

[46] W. Dallas, O. Polupan, S. Ostenpko, Resonance ultrasonic vibrations for crack detection in photovoltaic silicon wafers, Meas. Sci. Technol. 18 (3) (2007) 852.

[47] S. Nakatsuji, K. Dezao, H. Ichihashi, M. Matsukawa, Precise observation of ultrasonic pulses using an SPR sensor, IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 70 (6) (2023) 562-568.

[48] U. Eitner, M. Köntges, R. Brendel, Measuring thermomechanical displacements of solar cells in laminates using digital image correlation, in: 2009 34th IEEE Photovoltaic Specialists Conference, PVSC, 2009, 001280-4.

[49] J. Han, B. Pan, A novel method for measuring discontinuous deformation in digital image correlation based on partition and dividing strategy, Eng. Fract. Mech. 204 (2018) 185-197.

[50] X. Feng, P. Yang, L. He, G. Xing, M. Wang, W. Ke, Improved heterodyne system using double-passed acousto-optic frequency shifters for measuring the frequency response of photodetectors in ultrasonic applications, Opt. Express 28 (4) (2020) 4387-4397.

[51] C.A. Murray, N.A. Hoult, W.A. Take, Dynamic measurements using digital image correlation, Int. J. Phys. Model. Geotech. 17 (1) (2017) 41-52.

[52] T.J. Beberniss, D.A. Ehrhardt, High-speed 3D digital image correlation vibration measurement: Recent advancements and noted limitations, Mech. Syst. Signal Process. 86 (2017) 35-48.

[53] Y. Huang, J. Ji, K.M. Lee, Model-based digital image correlation for noncontact deformation measurement of strain field and mechanical property, IEEE Trans. Ind. Inf. 15 (9) (2019) 5109-5118.

[54] W. Wang, J.E. Mottershead, T. Siebert, A. Pipino, Frequency response functions of shape features from full-field vibration measurements using digital image correlation, Mech. Syst. Signal Process. 28 (2012) 333-347.

[55] S. Voutsinas, I. Mandourarakis, E. Koutroulis, D. Karolidis, I. Voyiatzis, M. Samarakou, Control and communication for smart photovoltaic arrays, in: Proceedings of the 26th Pan-Hellenic Conference on Informatics, 2022, pp. 139-144.

[56] W. Deng, W. Pei, Z. Qi, L. Kong, Design and implementation of data acquisition, communication and monitoring system for photovoltaic power station in microgrid, in: D.Y. Goswami, Y. Zhao (Eds.), Proceedings of ISES World Congress 2007, Vol. I-V, Springer Berlin Heidelberg, Berlin, Heidelberg, 2009, pp. 1538-1542.

[57] D.M. Hincapie, G.G. Duarte, S.B. Sepulveda, Low-cost and reliable wireless communication system for monitoring a photovoltaic source, in: IEEE Colombian Conference on Communication and Computing, IEEE COLCOM 2015, 2015, pp. 1-5.

[58] P.Q. Dzung, D.N. Dat, N.B. Anh, L.C. Hiep, H.H. Lee, Real-time communication network solution based on zigbee and ethernet for photovoltaic systems, in: 2014 9th IEEE Conference on Industrial Electronics and Applications, 2014, pp. 197-202.

[59] J.M. Paredes-Parra, A.J. García-Sánchez, A. Mateo-Aroca, Á. Molina-García, An alternative internet-of-things solution based on LoRa for PV power plants: Data monitoring and management, Energies 12 (5) (2019).

[60] A. Sabry, W. Hasan, M.A. Kadir, M. Radzi, S. Shafie, Wireless monitoring prototype for photovoltaic parameters, Indonesian J. Electr. Eng. Comput. Sci. (2018).

[61] C. Schuss, T. Fabritius, B. Eichberger, T. Rahkonen, Moving photovoltaic installations: Impacts of the sampling rate on maximum power point tracking algorithms, IEEE Trans. Instrum. Meas. 68 (5) (2019) 1485-1493.

[62] Z. Shi, B. Li, Z. Yang, X. Wang, AIC-based distributed photovoltaic data compression sensing method, in: 2023 8th International Conference on Intelligent Computing and Signal Processing, ICSP, 2023, pp. 370-373.

[63] L. Kang, Y. Shang, X. Liu, Y. Qin, Research on high-frequency data acquisition technology in distributed photovoltaic station areas, J. Phys.: Conf. Ser. 2474 (2023) 012039.

[64] N. JadHAV, S. Khande, Y. Shinde, Smart boiler system at power generation plant using power line carrier communication, Int. J. Comput. Appl. 176 (2020) 18-20.

[65] A.M. Eltamaly, M.A. Ahmed, M.A. Alotaibi, A.I. Alolah, Y.C. Kim, Performance of communication network for monitoring utility scale photovoltaic power plants, Energies 13 (21) (2020).

[66] L. Chen, X. Wang, Adaptive fault localization in photovoltaic systems, IEEE Trans. Smart Grid 9 (6) (2018) 6752-6763.

[67] A. Rivai, N. Rahim, M. Elias, J. Jamaludin, Multi-channel photovoltaic current-voltage (I-V) curve tracer employing adaptive-sampling-rate method, IET Sci. Meas. Technol. (2020) 969-976.

[68] A. Woyte, M. Richter, D. Moser, N. Reich, M. Green, S. Mau, et al., O & M Best Practices Guidelines, International Energy Agency IEA, 2014.

[69] S. Europe, Analytical Monitoring of Grid-Connected Photovoltaic Systems, SolarPower Europe, 2016.

[70] C. Schuss, B. Eichberger, T. Rahkonen, Impact of sampling interval on the accuracy of estimating the amount of solar energy, in: 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings, 2016, pp. 1-6.

[71] D. Murillo-Yarce, J. Alarcón-Alarcón, M.E. Rivera, C. Restrepo, J.A. Muñoz, C.R. Baier, et al., A review of control techniques in photovoltaic systems, Sustain. 12 (2020) 10598.

[72] S.R. Madeti, S.N. Singh, A comprehensive study on different types of faults and detection techniques for solar photovoltaic system, Sol Energy 158 (2017) 161-185.

[73] A. Triki-Lahiani, A. Bennani-Ben Abdelghani, I. Slama-Belkhodja, Fault detection and monitoring systems for photovoltaic installations: A review, Renew. Sustain. Energy Rev. 82 (2018) 2680-2692.

[74] A. Rivera, M. Theristis, E. Koumpli, S. Theocharides, G. Makrides, J. Sutterlueti, et al., Data processing and quality verification for improved photovoltaic performance and reliability analytics, Prog. Photovolt.: Res. Appl. 29 (2) (2021) 143-158.

[75] A. Hichri, M. Hajji, M. Mansouri, K. Abodayeh, K. Bouzrara, H. Nounou, et al., Genetic-algorithm-based neural network for fault detection and diagnosis: Application to grid-connected photovoltaic systems, Sustain. 14 (17) (2022).

[76] B.E. Meyers, E. Apostolaki-Iosifidou, L.T. Schelhas, Solar data tools: Automatic solar data processing pipeline, in: 2020 47th IEEE Photovoltaic Specialists Conference, PVSC, 2020, pp. 0655-0656.
[77] G. Blaesser, PV system measurements and monitoring the European experience, Sol. Energy Mater. Sol. Cells 47 (1) (1997) 167-176.

[78] R. Mukaro, X.F. Carelse, L. Olumekor, First performance analysis of a silicon-cell microcontroller-based solar radiation monitoring system, Sol Energy 63 (5) (1998) 313-321.

[79] L. Cristaldi, M. Faifer, A. Ferrero, A. Nechifor, Nechifor a on-line monitoring of the efficiency of photo-voltaic panels for optimizing maintenance scheduling, in: 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings, 2010, pp. 954-959.

[80] K.H. Chao, P.Y. Chen, M.H. Wang, C.T. Chen, An intelligent fault detection method of a photovoltaic module array using wireless sensor networks, Int. J. Distrib. Sens. Netw. 10 (5) (2014) 540147.

[81] M. Benganem, A. Maafi, Data acquisition system for photovoltaic systems performance monitoring, IEEE Trans. Instrum. Meas. 47 (1) (1998) 30-33.

[82] B. Wichert, M. Dymond, W. Lawrance, T. Friese, Development of a test facility for photovoltaic-diesel hybrid energy systems, J. Renew. Energy 22 (1) (2001) 311-319.

[83] E. Koutroulis, K. Kalaitzakis, Development of an integrated data-acquisition system for renewable energy sources systems monitoring, J. Renew. Energy 28 (1) (2003) 139-152.

[84] K. Papadakis, E. Koutroulis, K. Kalaitzakis, A server database system for remote monitoring and operational evaluation of renewable energy sources plants, J. Renew. Energy 30 (11) (2005) 1669-1669.

[85] N. Forero, J. Hernández, G. Gordillo, Development of a monitoring system for a PV solar plant, Energy Convers. Manag. 47 (15) (2006) 2329-2336.

[86] M. Demirtas, I. Sefa, E. Irmak, I. Colak, Low-cost and high sensitive microcontroller based data acquisition system for renewable energy sources, in: 2008 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, 2008, pp. 196-199.

[87] A. Purwadi, Y. Haroen, F.Y. Ali, N. Heryana, D. Nurafiat, A. Assegaf, Prototype development of a low cost data logger for PV based LED street lighting system, in: Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, 2011, pp. 1-5.

[88] M. Anwari, M.M. Dom, M.I.M. Rashid, Small scale PV monitoring system software design, Energy Procedia 12 (2011) 586-592.

[89] A. Mahjoubi, R.F. Mechlouch, A.B. Brahim, Data acquisition system for photovoltaic water pumping system in the desert of Tunisia, Procedia Eng. 33 (2012) 268-277.

[90] R. Eke, A. Sertap Kavasoglu, N. Kavasoglu, Design and implementation of a low-cost multi-channel temperature measurement system for photovoltaic modules, Meas. 45 (6) (2012) 1499-1509.

[91] M.E. Andreoni-López, F.J. Galdeano Mantiñan, M.G. Molina, Implementation of wireless remote monitoring and control of solar photovoltaic (PV) system, in: 2012 Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition, T & D-LA, 2012, pp. 1-6.

[92] G.M. Tina, A.D. Grasso, Remote monitoring system for stand-alone photovoltaic power plants: The case study of a PV-powered outdoor refrigerator, Energy Convers. Manag. 78 (2014) 862-871.

[93] J.T. Devaraju, K.R. Suhas, H.K. Mohana, V.A. Patil, Wireless portable microcontroller based weather monitoring station, Meas. 76 (2015) 189-200.

[94] F. Shariff, N.A. Rahim, W.P. Hew, Zigbee-based data acquisition system for online monitoring of grid-connected photovoltaic system, Expert Syst. Appl. 42 (3) (2015) 1730-1742.

[95] V. Villagrán, A. Montecinos, C. Franco, R.C. Muñoz, Environmental monitoring network along a mountain valley using embedded controllers, Meas. 106 (2017) 221-235.

[96] K.H. Chao, C.T. Chen, A remote supervision fault diagnosis meter for photovoltaic power generation systems, Meas. 104 (2017) 93-104.

[97] H. Rezk, I. Tyukhov, M. Al-Dhaifallah, A. Tikhonov, Performance of data acquisition system for monitoring PV system parameters, Meas. 104 (2017) 204-211.

[98] N. Sugiartha, I. Sugina, I. Putra, M. Indraswara, L. Suryani, Development of an arduino-based data acquisition device for monitoring solar PV system parameters, in: International Conference on Science and Technology, ICST 2018, 2019, pp. 1-5.

[99] M. Deriche, M.W. Raad, W. Suliman, An IOT based sensing system for remote monitoring of PV panels, in: 2019 16th International Multi-Conference on Systems, Signals & Devices, SSD, 2019, pp. 393-397.

[100] R.I.S. Pereira, S.C.S. Jucá, P.C.M. Carvalho, C.P. Souza, IoT network and sensor signal conditioning for meteorological data and photovoltaic module temperature monitoring, IEEE Lat. Am. 17 (06) (2019) 937-944.

[101] V. Gupta, M. Sharma, R. Pachauri, K. Babu, A low-cost real-time IOT enabled data acquisition system for monitoring of PV system, Energy Sources A: Recover. Util Env. Eff. 43 (2020) 1-16.

[102] N. Hassan, S. Ahammed, A. Islam, A study of IoT based real-time solar power remote monitoring system, IJASA 9 (2021) 27-36.

[103] S. Rompotis, J. Konstantaras, A. Ktena, L. Sarakis, C. Manasis, A monitoring system for PV plants using open technologies, in: 2022 IEEE 7th International Energy Conference, ENERGYCON, 2022, pp. 1-5.

[104] S. Priya, R. S, S. S, S. Malathi, An efficient monitoring scheme for stand-alone solar PV system using IoT, in: 2023 8th International Conference on Communication and Electronics Systems, ICSES, 2023, pp. 391-395.

[105] S.H. Sajjad, M. Erfanul Haque Sajib, M.N. Hasan, M. Abdur Razzak, Design and implementation of an IoT based solar power monitoring system, in: 2023 IEEE World AI IoT Congress, AIoT, 2023, pp. 0768-0773.

[106] G.M.T. Tchio, J. Kenfack, J. Joufo, Y.A. Mindzie, F.D. Menga, S.S. Ouro-Djobo, Development of a low-cost data acquisition system for analyzing the health of a photovoltaic system, TEM J. (2024) 2327-2340.

[107] E. Koumpli, Impact of Data Quality on Photovoltaic (PV) Performance Assessment (Doctoral thesis), Loughborough University, 2017.

[108] O.W. Compaore, G. Hoblos, Z. Koalaga, System faults diagnosis in a photovoltaic generator using artificial neural network approach, in: 2024 IEEE 8th Energy Conference, ENERGYCON, 2024, pp. 1-6.

[109] M. Mansouri, M. Trabelsi, H. Nounou, M. Nounou, Deep learning-based fault diagnosis of photovoltaic systems: A comprehensive review and enhancement prospects, IEEE Access. 9 (2021) 126286-306.

[110] S. Theocharides, G. Tziolis, J. Lopez-Lorente, G. Makrides, G.E. Georghiou, Impact of data quality on day-ahead photovoltaic power production forecasting, in: 2021 IEEE 48th Photovoltaic Specialists Conference, PVSC, 2021, pp. 0918-0922.

[111] A. Okere, M.T. Iqbal, A review of conventional fault detection techniques in solar PV systems and a proposal of long range (LoRa) wireless sensor network for module level monitoring, Eur. J. Electr. Eng. Comput. Sci. 4 (6) (2020) 30-45.

[112] W.G. Shin, J. Walters, et al., Best Practices Guide for Utility-Scale PV Monitoring with Intelligent Diagnostics using String-Level I-V Curves and Machine Learning, FSEC-CR-2119-22, FSEC, 2022.

[113] S. Ansari, A. Ayob, M.S.H. Lipu, et al., A review of monitoring technologies for solar PV systems using data processing modules and transmission protocols: Progress, Chall. Prospect. Sustain. 13 (15) (2021) 8120.

[114] A. Betti, M.L. Lo Trovato, F.S. Leonardi, et al., Predictive maintenance in photovoltaic plants with a big data approach, 2019, arXiv preprint.

[115] E. Quiles-Cucarella, P. Sánchez-Roca, I. Agustí-Mercader, Performance optimization of machine-learning algorithms for fault detection and diagnosis in PV systems, Electron. 14 (9) (2025) 1709.

[116] M. Alajmi, I. Abdel-Qader, Fault detection and localization in solar photovoltaic arrays using the current-voltage sensing framework, in: 2016 IEEE International Conference on Electro Information Technology, EIT, 2016, pp. 0307-0312.

[117] T. Tajwar, O. Hassan Mobin, F.R. Khan, S.F. Hossain, M. Islam, M. Mosaddequr Rahman, Infrared thermography based hotspot detection of photovoltaic module using YOLO, in: 2021 IEEE 12th Energy Conversion Congress & Exposition - Asia, ECCE-Asia, 2021, pp. 1542-1547.

[118] A. Rocky, M. Burhanzoi, O. Kenta, T. Ikegami, S. Kawai, Photovoltaic module fault detection using integrated magnetic sensors, IEEE J. Photovolt. 9 (6) (2019) 1783-1789.

[119] I.M. Kwembur, J.L. Crozier McCleland, E.E. van Dyk, F.J. Vorster, Detection of potential induced degradation in mono and multi-crystalline silicon photovoltaic modules, Phys B: Condens. Matter 581 (2020) 411938.

[120] F. Harrou, Y. Sun, A. Dorbane, B. Bouyeddou, Sensor fault detection in photovoltaic systems using ensemble learning-based statistical monitoring chart, in: 2023 11th International Conference on Smart Grid, icSmartGrid, 2023, pp. 1-6.

[121] Q. Xiong, S. Ji, X. Liu, X. Feng, F. Zhang, L. Zhu, et al., Detecting and localizing series arc fault in photovoltaic systems based on time and frequency characteristics of capacitor current, Sol Energy 170 (2018) 788-799.

[122] M. Omaña, M. Grossi, C. Metra, Early detection of photovoltaic system inverter faults, Microelectron. Reliab. 135 (2022) 114594.

[123] D.C. Miller, R.L. Arnold, P.L. Hacke, C.S. Jiang, S.C. Hayden, H. Moutinho, et al., Photovoltaic cable connectors: A comparative assessment of the present state of the industry, IEEE J. Photovolt. 14 (5) (2024) 793-802.

[124] R.R. Xie, T. Zheng, Z.K. Zhang, P.Z. Zhang, Voltage based protection for 10kV distribution network with photovoltaic integration, in: 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC, 2016, pp. 2429-2433.

[125] A. Sinha, RG, Effects of different excitation waveforms on detection and characterisation of delamination in PV modules by active infrared thermography, Nondestruct. Test. Eval. 32 (4) (2017) 418-434.

[126] M. Dhimish, V. Holmes, Solar cells micro crack detection technique using state-of-the-art electroluminescence imaging, J. Sci.: Adv. Mater. Devices 4 (4) (2019) 499-508.

[127] M. Liggett, D.J. Colvin, B. Babu, W.C. Oltjen, X. Yu, M. Matam, et al., Characterization of field exposed photovoltaic modules featuring signs of contact degradation, in: 2023 IEEE 50th Photovoltaic Specialists Conference, PVSC, 2023, pp. 1-3.

[128] C. Li, Y. Yang, K. Zhang, C. Zhu, H. Wei, A fast MPPT-based anomaly detection and accurate fault diagnosis technique for PV arrays, Energy Convers. Manag. 234 (2021) 113950.

[129] T. Hamada, T. Azuma, I. Nanno, N. Ishikura, M. Fujii, S. Oke, Impact of bypass diode fault resistance values on burnout in bypass diode failures in simulated photovoltaic modules with various output parameters, Energies 16 (16) (2023).
[130] A.F. Amiri, H. Oudira, A. Chouder, S. Kichou, Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier, Energy Convers. Manag. 301 (2024) 118076.

[131] R.W. Bello, P.A. Owolawi, E.A. van Wyk, C. Du, Photovoltaic module dataset for automated fault detection and analysis in large photovoltaic systems using photovoltaic module fault detection, Data Br. 57 (2024) 111184.

[132] P.R. Satpathy, B. Aljafari, S.B. Thanikanti, S.R.K. Madeti, Electrical fault tolerance of photovoltaic array configurations: Experimental investigation, performance analysis, monitoring and detection, Renew. Energy 206 (2023) 960-981.

[133] F.S.M. Abdallah, M.N. Abdullah, I. Musirin, A.M. Elshamy, Intelligent solar panel monitoring system and shading detection using artificial neural networks, Energy Rep. 9 (2023) 324-334.

[134] C. Kapucu, M. Cubukcu, A supervised ensemble learning method for fault diagnosis in photovoltaic strings, J. Energy 227 (2021) 120463.

[135] Y. Zhao, R. Ball, J. Mosesian, J.F. de Palma, B. Lehman, Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays, IEEE Trans. Power Electron. 30 (5) (2015) 2848-2858.

[136] A.E. Lazzaretti, CHd. Costa, M.P. Rodrigues, G.D. Yamada, G. Lexinoski, G.L. Moritz, et al., A monitoring system for online fault detection and classification in photovoltaic plants, Sensors 20 (17) (2020).

[137] A. Livera, G. Paphitis, L. Pikolos, I. Papadopoulos, J. Montes-Romero, J. Lopez-Lorente, et al., Intelligent cloud-based monitoring and control digital twin for photovoltaic power plants, in: 2022 IEEE 49th Photovoltaics Specialists Conference, PVSC, 2022, pp. 0267-0274.

[138] J. Choi, B. Ahn, G. Bere, S. Ahmad, H.A. Mantooth, T. Kim, Blockchain-based man-in-the-middle (MITM) attack detection for photovoltaic systems, in: 2021 IEEE Design Methodologies Conference, DMC, 2021, pp. 1-6.

[139] A. Kajmakovic, K. Diwold, K. Römer, J. Pestana, N. Kajtazovic, Degradation detection in a redundant sensor architecture, Sensors 22 (12) (2022).

[140] B.P. Kumar, D.S. Pillai, N. Rajasekar, M. Chakkarapani, G.S. Ilango, Identification and localization of array faults with optimized placement of voltage sensors in a PV system, IEEE Trans. Ind. Electron. 68 (7) (2021) 5921-5931.