# Artificial intelligence in photovoltaic fault diagnosis: A Natural Language-Based Topic-tSNE Fusion analysis

Edgar Hernando Sepúlveda-Oviedo, Louise Travé-Massuyès, Audine Subias, Marko Pavlov, Corinne Alonso

► To cite this version:

Edgar Hernando Sepúlveda-Oviedo, Louise Travé-Massuyès, Audine Subias, Marko Pavlov, Corinne Alonso. Artificial intelligence in photovoltaic fault diagnosis: A Natural Language-Based Topic-tSNE Fusion analysis. Energy and AI, 2025, 21, pp.100558. 10.1016/j.egyai.2025.100558. hal-05251841

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Submitted on 12 Sep 2025

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

# Artificial intelligence in photovoltaic fault diagnosis: A Natural Language-Based Topic-tSNE Fusion analysis

Edgar Hernando Sepúlveda-Oviedo<sup>a ID,*</sup>, Louise Travé-Massuyès<sup>a</sup>, Audine Subias<sup>a</sup>, Marko Pavlov<sup>b</sup>, Corinne Alonso<sup>a</sup>

<figure><img src="image_2.png" /></figure>

## GRAPHICAL ABSTRACT

<figure><img src="image_3.png" /></figure>

I) The approach addresses the subjectivity found in conventional literature reviews, providing an unbiased classification of fault detection methods.

II) The methodology categorizes fault detection approaches based on data-driven and model-based techniques, enhancing the clarity of methodological distinctions.

III) This method includes additional data insights from research databases, improving the contextual richness of the analysis.

IV) The TOPIC-tSNE Fusion<sup>a</sup> method exposes hidden semantic relationships in the literature, enhancing the understanding of fault diagnosis trends across both supervised, unsupervised, semi-supervised, and reinforcement learning approaches.

V) Key physical variables and primary faults identified in PV literature are systematically outlined, contributing to a more comprehensive fault diagnostic framework.

Artificial Intelligence in
PV Fault Diagnosis:
A Topic-tSNE Fusion Analysis

## HIGHLIGHTS

* Novel data-driven method reduces subjectivity in traditional reviews.

* The proposed approach is versatile and applicable across different domains.

* Hidden semantic links often overlooked in reviews are uncovered.

* Essential physical variables for fault diagnosis are clearly outlined.

* Frequently detected faults in the state of the art are prominently highlighted.

## ARTICLE INFO

**Keywords:**

Photovoltaic fault diagnosis

Machine learning

Topic modeling

t-SNE visualization

PV system reliability

Artificial intelligence

## ABSTRACT

Timely fault detection in photovoltaic systems is critical for ensuring energy efficiency, reliability, and cost-effectiveness. However, the nonlinear and weather-dependent behavior of photovoltaic systems poses challenges for accurate diagnosis. This study presents a large-scale review of 983 scientific publications on artificial intelligence-based photovoltaic fault detection, using a novel methodology called Topic-tSNE Fusion. This approach integrates topic modeling, dimensionality reduction, and expert analysis to extract and visualize dominant research themes. Four key machine learning paradigms are identified: supervised, unsupervised, semi-supervised, and reinforcement learning. Among them, supervised methods, particularly neural networks and support vector machines, are the most frequently applied, showing accuracies above 95% in controlled

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

<footnote><sup>b</sup> Feedgy, Paris, France</footnote>

<footnote>* Corresponding author.
E-mail address: ehsepulved@laas.fr (E.H. Sepúlveda-Oviedo).</footnote>
conditions. The analysis also reveals growing use of semi-supervised and hybrid approaches to overcome data scarcity. Commonly monitored variables include irradiance, voltage, and current, while the most studied faults are shading, open-circuit, and degradation. Several open-access datasets supporting fault diagnosis research are catalogued. Overall, the proposed method enables a more objective and scalable review process and uncovers emerging trends, such as the shift toward lightweight artificial intelligence for edge deployment and frugal diagnostic architectures. The methodology is scalable and adaptable to other domains facing similar challenges in knowledge synthesis and system monitoring.

# 1. Introduction

Over recent decades, Photovoltaic (PV) energy has become a cornerstone of sustainable energy solutions, with a significant increase in global PV capacity due to advancements in technology and reductions in manufacturing costs [1]. As PV systems scale up, their vulnerability to faults increases, making reliable and efficient fault detection crucial for ensuring energy yield and long-term system stability [2].

In the broader context of the global energy transition, enhancing the operational efficiency and resilience of PV systems is critical for meeting growing clean energy demands. Faults not only reduce energy production efficiency but also undermine the economic and environmental benefits of photovoltaic investments.

PV systems are subject to a wide variety of faults that can significantly degrade performance. Detecting these faults is complex due to the nonlinear and weather-dependent behavior of solar energy systems. Traditional fault detection methods such as manual visual inspections and parameter-based assessments have proven limited. Manual inspections are resource-intensive and impractical for large or remote installations [3], while parameter-based approaches often struggle with noisy signals and can produce false positives under changing environmental conditions [4].

In response, more advanced techniques have been proposed, including wavelet transformations, neuro-fuzzy algorithms [5], collaborative detection methods [6], and deep learning with thermal imaging [7]. While these have improved accuracy and adaptability, they remain constrained by issues such as training data requirements, computational demands, and generalization challenges.

Artificial Intelligence (AI) and Machine Learning (ML) approaches, including Support Vector Machine (SVM) and K-nearest neighbor (KNN), have shown promise by automating fault classification based on operational data [8]. However, limitations persist, particularly when detecting early, stage or infrequent faults where high false positive rates reduce reliability [9].

AI-driven diagnostic systems have the potential to transform the energy sector by enabling predictive maintenance, minimizing downtime, and optimizing energy output in real time. These capabilities are vital to ensure the scalability and long-term reliability of AI-powered renewable energy infrastructures.

Recent literature reviews have explored fault detection strategies in PV systems [10], but many are constrained by subjective article selection and scalability issues. These reviews often struggle to capture emerging trends or provide comprehensive overviews across large corpora due to methodological limitations.

To address these gaps, objective and scalable review methodologies have emerged, employing either bibliometric analysis or topic modeling to structure large bodies of research. These approaches provide more rigorous insights into thematic trends and research directions [11].

In this context, we propose a novel methodology called Topic-tSNE Fusion, which combines Latent Dirichlet Allocation (LDA) for topic modeling and T-distributed stochastic neighbor embedding (TSNE) for visualization. Applied to a dataset of 983 publications on AI-based fault diagnosis in PV systems, this method enables a systematic and objective review that reduces reviewer subjectivity and enhances thematic clarity.

By bridging the fields of energy systems engineering and artificial intelligence, this work contributes to building more intelligent, reliable,

and sustainable PV infrastructures, aligning with current priorities in the Energy and AI research community.

The key contributions of this study are:

1. Introducing a replicable and scalable methodology for reducing the subjectivity of traditional reviews.

2. Structuring fault diagnosis approaches into data-driven and model-based categories.

3. Enriching the analysis with metadata from research databases to contextualize the results.

4. Revealing hidden semantic relationships in the state of the art across learning paradigms (supervised, unsupervised, semi-supervised, reinforcement).

5. Identifying key diagnostic variables, fault types, and open-access datasets that support future research.

The remainder of the paper is organized as follows: Section 2 reviews existing work on PV fault detection and topic modeling. Section 3 details the methodology. Section 4 presents the results of the topic modeling analysis. Section 5 discusses implications, challenges, and research directions. Section 6 concludes the study.

# 2. Related work

Numerous reviews have explored fault detection methodologies in PV systems [12]. While these reviews provide a broad understanding, they are often limited by the subjective selection of articles and the number of documents that can be analyzed manually. Traditional literature reviews rely on manual curation and selection, which inherently introduces biases and limits the scale of analysis. For example, the study by Et-taleby et al. (2023) reviews the application of machine learning algorithms in PV fault detection, identifying various models but is limited by the scope and selection of studies considered [13].

To address these limitations, quantitative methods such as bibliometric analysis have been proposed. These methods quantify research trends based on keyword analysis but often lack contextual depth, failing to capture the nuanced relationships among research topics [14]. An alternative approach is text mining with machine learning techniques, specifically LDA for topic modeling. LDA provides a more nuanced understanding of the research landscape by uncovering hidden topics and their relationships within large datasets [15]. Unlike simple clustering methods, topic modeling offers greater flexibility and efficiency in handling diverse topics across multiple fields, including fault diagnosis in PV systems [16].

In recent years, topic modeling has also been employed to conduct large-scale, systematic literature reviews. For instance, Asmussen and Møller (2019) developed a framework using LDA to analyze vast collections of research papers for exploratory literature reviews, demonstrating that topic modeling can streamline and objectify the review process by organizing and categorizing literature at scale [17]. This approach supports the review of larger datasets, enabling a more comprehensive understanding of research trends and topic relations.

PV systems, central to this study, are comprised of multiple components, each with specific failure modes that can impact overall performance. The core of any PV system is the PV cell, which converts sunlight into electricity through the PV effect. These cells are arranged in series or parallel configurations to form modules, which are then encapsulated to protect against environmental degradation [18]. Fig. 1 illustrates a typical arrangement of PV cells within a module.
<figure><img src="image_4.png" /><figcaption>Fig. 1. Details of a PV module with series-connected cells and associated bypass diodes.</figcaption></figure>

Beyond the modules, essential components include inverters (which convert direct current to alternating current), charge controllers for battery regulation in off-grid systems, and transformers in large-scale installations to match grid voltage [19]. Common fault modes in PV systems include cell cracking, hot spots, and inverter malfunctions, each of which poses risks to system efficiency and safety [20]. Reliable fault diagnosis is crucial to prevent operational disruptions and maintain performance [21].

As extensively studied in recent literature, faults in photovoltaic systems may arise not only from hardware failures but also from external environmental factors. On the one hand, operational faults can result from issues such as improper installation, component degradation, or inverter failures, as detailed in [22]. On the other hand, environmental conditions, including soiling, humidity, irradiance variation, and temperature extremes, also contribute significantly to system performance degradation, as shown in [23]. These two dimensions of fault origin underscore the complexity of fault diagnosis and the need for robust and adaptive approaches in both data-driven and physics-based strategies.

## 3. Methodology

The methodology developed in this study combines LDA and TSNE within a structured framework referred to as Topic-tSNE Fusion. This approach is designed to provide a systematic, scalable, and less subjective framework for synthesizing large datasets. By leveraging machine learning for topic modeling and high-dimensional data visualization, this methodology addresses biases commonly associated with traditional review methods and enhances analytical capacity.

The Topic-tSNE Fusion methodology follows a structured sequence of steps organized according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol [24]. These steps, illustrated in Fig. 2, include data collection, topic modeling, visualization using TSNE, and qualitative content analysis. Each step has been implemented to increase transparency, reproducibility, and interpretability, offering a more robust foundation for understanding research trends in PV fault detection.

### 3.1. Data collection

To ensure the robustness and objectivity of the Topic-tSNE Fusion approach, careful attention was given to constructing a high-quality data corpus, as the reliability of topic modeling is closely linked to the representativeness of the dataset [25]. The data collection process was guided by a Boolean search strategy, built upon four thematic keyword groups defined by domain experts: (i) photovoltaic

<figure><img src="image_5.png" /><figcaption>Fig. 2. The Topic-tSNE Fusion methodology, including data collection, topic modeling, TSNE, and qualitative analysis.</figcaption></figure>

systems, (ii) artificial intelligence, (iii) fault-related terminology, and (iv) diagnosis-related concepts.

This search logic is illustrated in Fig. 3, which details the structured pipeline used to retrieve, filter, and refine the final document corpus. The conceptual Boolean structure guiding the search can be expressed as in Eq. (1).

$$Q = K_{pv} \text{ AND } K_{ai} \text{ AND } K_{fault} \text{ AND } K_{diagnosis} \quad (1)$$

where:

$$K_{pv} = (\text{"photovoltaic" OR "PV"})$$

$$K_{ai} = (\text{"artificial intelligence" OR "machine learning"})$$

$$K_{fault} = (\text{"failure" OR "anomaly" OR "fault"})$$

$$K_{diagnosis} = (\text{"detection" OR "classification" OR "diagnosis"})$$

This Boolean query was applied to the titles, abstracts, and keywords of documents indexed in five major databases: Scopus, Web of Science, IEEE Xplore, ScienceDirect, and SpringerLink. While the syntax was adapted slightly between platforms, the logical structure was preserved to ensure consistency. An example of the actual search string used in IEEE Xplore is shown below for transparency and reproducibility:

```
('"All Metadata": 'photovoltaic' OR 'All Metadata':
'"PV"') AND ('"All Metadata": 'artificial intelligence'
OR '"All Metadata": 'machine learning') AND ('"All Meta-
data": 'failure' OR 'All Metadata': 'anomaly' OR 'All
Metadata': 'fault') AND ('"All Metadata": 'detection' OR
'"All Metadata": 'classification' OR 'All Metadata':
'diagnosis')
```

The initial search across the five databases retrieved a total of 10,382 documents: 641 from IEEE Xplore, 515 from Scopus, 518 from Web of Science, 3779 from SpringerLink, and 4929 from ScienceDirect. After merging all records, automated deduplication was performed using Zotero [26], which identified and removed 122 redundant entries, resulting in 10,260 unique documents.

To manage the large volume and ensure relevance, a representative sampling strategy was then applied. Based on keyword presence, thematic alignment, and preliminary screening of titles and abstracts, a set of 1,083 documents was selected for further analysis.

Next, a rigorous screening process was conducted to apply the inclusion and exclusion criteria summarized in Table 1. These criteria were designed to ensure that the final dataset focused on high-quality, relevant, and peer-reviewed literature in the field of AI-based fault diagnosis in photovoltaic systems.
<figure><img src="image_6.png" /><figcaption>Fig. 3. Data collection process for creating the data corpus: building the search equation, retrieving documents from Scopus, WoS, IEEE Xplore, ScienceDirect, and SpringerLink, applying selection criteria, adding documents from other sources, and final corpus construction.</figcaption></figure>

<table><caption>**Table 1**
Inclusion and exclusion criteria for article selection.</caption><thead><tr><td>Inclusion criteria</td><td>Exclusion criteria</td></tr></thead><tbody><tr><td>Peer-reviewed journal articles, conference papers, and book chapters</td><td>Thesis, dissertations, technical reports, and non-peer-reviewed documents</td></tr><tr><td>Publications in English language</td><td>Publications in languages other than English</td></tr><tr><td>Focus on artificial intelligence or machine learning applied to PV fault diagnosis</td><td>Studies not related to PV systems or not involving diagnostic techniques</td></tr><tr><td>Publication years from 2010 to 2024</td><td>Documents outside the 2010–2024 range</td></tr><tr><td>Accessible through Scopus, Web of Science, or expert selection</td><td>Documents from unverified sources</td></tr></tbody></table>

During this screening, 150 documents were excluded: 42 due to language (non-English), 38 because they did not meet the publication type requirement (e.g., theses, reports), 27 for being outside the date range, and 43 for lacking direct relevance to PV fault diagnosis or AI techniques. This resulted in a refined set of 933 documents.

To further enhance completeness, 50 additional expert-recommended documents were incorporated. These articles addressed seminal developments or niche applications not captured by the automated search but considered crucial for the study. All expert-sourced documents adhered to the same inclusion standards.

The final dataset thus comprised 983 documents, forming a robust and representative basis for the topic modeling process. As shown in Fig. 4, the corpus reflects both the distribution by document type and the increasing trend in research output between 2010 and 2024. This growth highlights the expanding role of AI in PV fault detection and its growing importance within the broader context of renewable energy systems.

### 3.1.1. Dataset representativeness and bias mitigation

To ensure the representativeness of the selected dataset, a multi-source strategy was implemented. The study utilized both Scopus and Web of Science, two of the most comprehensive bibliographic databases, to reduce dependency on a single index and improve regional and disciplinary diversity. The Boolean search query was carefully crafted and validated by domain experts to ensure inclusiveness of relevant keywords and terminologies, enhancing the coverage of different AI and PV fault diagnosis techniques.

To mitigate potential publication biases, several measures were adopted. First, only peer-reviewed documents, including journal articles, conference papers, and book chapters, were included regardless of geographical origin. Although the search was limited to English-language publications for standardization, this was acknowledged as a

limitation (see Section 5.5). To counterbalance this, a small number of expert-sourced documents were included, particularly those that might not be retrieved by automated queries but were deemed seminal. All included expert-selected documents met the same inclusion criteria to maintain consistency.

These actions, together with the removal of duplicates and the application of consistent selection filters, helped build a diverse and representative dataset of 983 documents, minimizing bias and enhancing the robustness of subsequent analyses.

### 3.1.2. Data pre-processing

To maximize the clarity and coherence of topics generated by LDA, a comprehensive data pre-processing stage was conducted, structured in four key steps. First, tokenization was applied to segment the text into sentences and words. This step was followed by case normalization (conversion to lowercase) and the removal of punctuation marks, ensuring consistency in the dataset.

Second, non-contributory words, or “stop words”, were filtered out, including commonly used words as well as domain-specific terms that do not add semantic value. This step also involved removing email addresses, URLs, and any irrelevant numerical tokens. The stop words used for this study were sourced from the Natural Language Toolkit (NLTK) library and validated by domain experts to ensure contextual accuracy.

Third, lemmatization and stemming were employed to standardize word forms, transforming words into their base forms. For instance, terms like “studies”, “studied”, and “studying” were normalized to “study”, enhancing topic coherence by reducing word redundancy.

Finally, frequently occurring bigrams and trigrams that represent domain-specific concepts, such as “battery_energy_storage” and “renewable_energy”, were identified and preserved to capture nuanced relationships in the data. This pre-processing strategy not only improves
<figure><img src="image_7.png" /><figcaption>Fig. 4. Document statistics: distribution by type, and annual distribution of the 983 publications reviewed, covering the period 2010–2024.</figcaption></figure>

topic coherence but also enhances the reproducibility of the study, as each step is clearly defined and supported by tools such as NLTK, Scikit-learn, and Pandas, all implemented in Python 3.7.9 [27].

With the data corpus pre-processed and finalized, the Topic-tSNE Fusion methodology proceeded to the topic modeling phase, as detailed in Section 3.2.

## 3.2. Topic modeling

In this study, LDA is applied in the topic modeling phase of the Topic-tSNE Fusion approach to generate “topics” that reflect the intellectual structure of the literature on PV system fault diagnosis. Topic modeling is a quantitative statistical method that allows semantic information to be extracted from large text datasets, revealing hidden thematic patterns that might otherwise be overlooked in traditional analysis [28]. Unlike simpler clustering methods, LDA uses a probabilistic approach to model each document as a mixture of topics, making it more flexible and robust for capturing complex relationships within the data [29].

The use of LDA is particularly beneficial for thematic extraction in PV fault diagnosis due to its probabilistic nature, which assigns words in documents to topics based on likelihood, providing a richer contextual understanding [30]. This method surpasses deterministic approaches, such as Latent Semantic Analysis (LSA) or simple clustering, by offering superior coherence and interpretability, essential for capturing complex, overlapping topics in fault detection literature [17]. Moreover, LDA has demonstrated consistent performance across various fields, from communication studies to software engineering, validating its adaptability to fault diagnosis contexts where intricate and domain-specific language is prevalent [31].

LDA was chosen over alternatives such as Non-negative Matrix Factorization (NMF), Bidirectional Encoder Representations Topic Modeling (BERTopic), or Structural Topic Modeling (STM) due to its proven performance in capturing latent topics across large and heterogeneous corpora, its robustness to noise in domain-specific language, and its interpretable probabilistic foundation [32]. Unlike NMF, which may struggle with overlapping topics due to its additive nature, LDA allows documents to exhibit multiple thematic affiliations [33]. Additionally, while BERTopic leverages contextual embeddings, it introduces

increased computational overhead and is more sensitive to hyperparameter tuning, which can affect reproducibility [34]. STM, although powerful for incorporating covariates, was not used here to maintain focus on unsupervised discovery across diverse document sources without additional metadata [35].

### 3.2.1. Latent Dirichlet allocation process

The LDA model assumes that each document is composed of various underlying topics. Each topic $k_p$, where $p = 1, ..., K$, has a specific probability distribution of words. Given a corpus $D$ of $M$ documents, each with $N_{m_i}$ words, LDA follows a generative process to allocate topics as follows:

1. Choose a multinomial distribution $\phi_k$ for each topic $k$ from a Dirichlet distribution with parameter $\beta$.

2. Choose a multinomial distribution $\theta_{m_i}$ for each document $m_i$ from a Dirichlet distribution with parameter $\alpha$.

3. For each word $w_n$ in document $m_i$:

• Sample a topic $k_{j,n}$ from $\theta_{m_i}$.

• Sample a word $w_{j,n}$ from $\phi_k$ conditioned on the selected topic.

The probability of the observed data corpus $D$, given parameters $\alpha$ and $\beta$, is calculated as given in Eq. (2).

$$a(\theta_{m_i}) = \prod_{m_i=1}^{M} \int_{\theta} p(\theta_{m_i} | \alpha),$$

$$b(\theta_{m_i}) = \prod_{n=1}^{N_{m_i}} \sum_{k_{j,n}} p(k_{j,n} | \theta_{m_i}) p(w_{j,n} | k_{j,n}, \beta),$$

$$p(D | \alpha, \beta) = a(\theta_{m_i}) * b(\theta_{m_i}) \ d\theta_{m_i}, \qquad (2)$$

where $\alpha$ and $\beta$ are hyperparameters that influence topic distributions at the document and corpus levels. Determining the optimal number of topics $K$ is essential to maximize coherence, a measure of the internal consistency of word associations within a topic. In this study, the Mallet library is used for its efficiency and precision in implementing LDA [36].
<figure><img src="image_8.png" /><figcaption>Fig. 5. Topic coherence analysis based on the coherence score using the metric ($C_V$). The optimal number of topics is 4.</figcaption></figure>

### 3.2.2. Topic coherence measurement

The quality of the generated topics is evaluated through topic coherence, particularly using the $C_V$ metric, known for its effectiveness in balancing interpretability and internal consistency [37]. Topic coherence measures assess the semantic similarity of words within a topic, with higher scores indicating greater thematic cohesion. Fig. 5 illustrates coherence values across different models tested, guiding the selection of four topics as optimal. This step ensures the robustness and interpretability of the generated topics for fault diagnosis in PV systems.

While coherence is widely used in applied topic modeling, it is often compared to another common metric: perplexity, which is based on statistical likelihood. Although perplexity is a standard likelihood-based metric for topic modeling, several studies have shown that it does not reliably reflect the human interpretability of topics. In contrast, coherence-based metrics such as $C_V$ better align with expert evaluation of topic quality. For example, Ding et al. [38] demonstrate that coherence-aware models generate more semantically meaningful topics than those optimized for perplexity. Likewise, Glendowne and Glendowne [39] report that coherence metrics are more consistent with human judgments of interpretability, while perplexity often favors overly granular or fragmented topics. Based on these insights and our preliminary experimentation, we prioritized $C_V$ coherence for model selection.

In addition, the resulting topics were reviewed by domain experts in PV fault diagnosis to ensure thematic clarity and relevance. This human-in-the-loop validation complements the coherence metric and reinforces the interpretability of the selected topic structure.

The probabilistic framework of LDA and coherence scoring provide significant advantages for the Topic-t SNE Fusion approach. Its flexibility in topic assignment enables accurate thematic extraction across diverse PV system documents, addressing both relevance and specificity in fault diagnosis topics. This robust methodology, complemented by visualization through TSNE in Section 3.3, enhances interpretability and offers a comprehensive thematic overview of the state of the art on PV fault detection.

### 3.3. t-distributed Stochastic Neighbor Embedding (t-SNE)

The TSNE algorithm is a robust tool for visualizing high-dimensional data in lower-dimensional spaces, such as two or three dimensions. TSNE builds on the foundational Stochastic Neighbor Embedding (SNE) method introduced by Hinton and Rowels [40], which translates distances between points in high-dimensional space into conditional probabilities representing point similarity. By preserving these relationships

in a low-dimensional map, TSNE enables interpretable, visually distinct clusters that reveal complex patterns within large datasets.

To quantify similarity, TSNE calculates the conditional probability $p_{j|i}$ between two high-dimensional points $x_i$ and $x_j$, denoting the probability that $x_i$ selects $x_j$ as a neighbor. For simplification, self-similarity values $p_{i|i}$ are set to zero [41]. The conditional probability $p_{j|i}$ is derived from a Gaussian kernel as given in Eq. (3).

$$p_{j|i} = \frac{\exp(-\|x_i - x_j\|^2/2\sigma_i^2)}{\sum_{k \neq i} \exp(-\|x_i - x_k\|^2/2\sigma_k^2)}, \quad (3)$$

where $\|x_i - x_j\|$ represents the Euclidean distance between $x_i$ and $x_j$, and $\sigma_i^2$ denotes the Gaussian variance centered on $x_i$. Due to variability in data density, a universal $\sigma_i^2$ is not ideal; instead, local adjustment provides greater accuracy. For instance, regions with dense data benefit from a smaller $\sigma_i^2$ than sparser areas [42]. The SNE algorithm optimizes $\sigma_i^2$ using a binary search, generating a distribution $P_i$ with a defined perplexity level. Perplexity, commonly set between 5 and 50, approximates the effective number of neighbors as given in Eq. (4).

$$Perp(P_i) = 2^{H(P_i)}, \quad (4)$$

where $H(P_i)$, calculated from Eq. (5), is the Shannon entropy of $P_i$ in bits:

$$H(P_i) = - \sum_j p_{j|i} \log_2(p_{j|i}). \quad (5)$$

In the reduced-dimensional map, TSNE computes a corresponding conditional probability $q_{j|i}$ between low-dimensional points $y_i$ and $y_j$, which represent $x_i$ and $x_j$, respectively. To enhance stability, TSNE replaces the Gaussian with a Student-t distribution for calculating $q_{j|i}$ across the mapped points as given in Eq. (6).

$$q_{ij} = \frac{(1 + \|y_i - y_j\|^2)^{-1}}{\sum_{k \neq i} (1 + \|y_k - y_i\|^2)^{-1}}. \quad (6)$$

The goal of TSNE is to minimize the divergence between $p_{j|i}$ and $q_{j|i}$, measured using Kullback-Leibler (KL) divergence and optimized through gradient descent. The cost function $C_{P\|Q}$ to be minimized is defined as in Eq. (7).

$$C_{P\|Q} = \text{KL}(P \| Q) = \sum_{i} \sum_{j} p_{ij} \log \frac{p_{ij}}{q_{ij}}. \quad (7)$$

To satisfy the symmetry requirement, Eq. (8) ensures balanced contributions to the probability structure, thereby reinforcing interpretability:

$$p_{ij} = \frac{p_{j|i} + p_{i|j}}{2n}. \quad (8)$$

With these adjustments, TSNE produces clusters that preserve the relational integrity of high-dimensional data while enabling distinct separation in a low-dimensional visual. This interpretability is especially valuable in complex data applications, such as fault diagnosis, where clear visual groupings aid in understanding underlying data structures.

### 3.3.1. Cluster evaluation with Silhouette coefficient

The silhouette coefficient $\phi$ provides a measure of cluster cohesion and separation, indicating how well each sample fits within its cluster compared to other clusters. Ranging from -1 to 1, a high $\phi$ value suggests strong within-cluster cohesion and clear separation from other clusters [43]. For cluster $g$, the average silhouette coefficient $\tilde{\phi}_g$ is defined as in Eq. (9).

$$\tilde{\phi}_g = \frac{1}{n_e} \sum_{i=1}^{n_e} \frac{b(i) - a(i)}{\max\{a(i), b(i)\}}, \quad (9)$$

where $a(i)$ is the mean distance of sample $e_i$ to other points within the same cluster $g$, and $b(i)$ is the minimum average distance of $e_i$ to points in other clusters. A high value for $a(i)$ indicates good intra-cluster compactness, and a high $b(i)$ reflects strong inter-cluster separation.
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Distribution of Document Word Counts by Dominant Topic. The central panel shows the 2D TSNE visualization of the four main topics, with corresponding word clouds for each. Average silhouette coefficients, $\bar{\phi}_g$, are shown for each topic cluster $g$.</figcaption></figure>

In Fig. 6, clusters formed by TSNE exhibit high average silhouette coefficients, indicating effective grouping of related documents. This robust clustering approach, along with high interpretability, confirms TSNE as a valuable tool for thematic analysis and exploration in fault diagnosis research.

# 4. Qualitative analysis of the content of the results

The Topic-tSNE Fusion methodology facilitated a comprehensive, structured exploration of the state-of-the-art in fault diagnosis for PV systems through machine learning. This approach not only allowed for the identification of main thematic topics but also revealed additional layers of insight essential for understanding current advancements in PV diagnosis.

Notably, the TSNE visualizations revealed four well-defined and separable topic clusters, each associated with specific methodological approaches and fault types. These clusters not only validated the coherence of the topics extracted through LDA, but also highlighted hidden semantic relationships in the state of the art, such as the affinity between Semi-Supervised Learning (SSL) methods and certain types of faults, such as soiling or partial shading. These insights demonstrate the strength of dimensionality reduction techniques in uncovering thematic boundaries and emerging trends not easily captured by traditional reviews.

Specifically, the analysis yielded three main types of results that are explored in detail in this section.

First, the methodology identified four major topic categories in machine learning approaches for PV fault diagnosis: Supervised Learning (SL), SSL, Reinforcement Learning (RL), and Unsupervised Learning (UL). These categories provide distinct groupings, each with unique techniques, strengths, and limitations, that collectively capture the application landscape of machine learning algorithms in PV fault diagnosis. The topic categories and their key characteristics are discussed in Section 4.1.

In addition to the primary topic clusters, the Topic-tSNE Fusion approach highlighted two significant methodological approaches that frequently emerged across the identified topics: data-driven and model-based approaches. These approaches provide different frameworks within PV fault detection, with data-driven methods focusing on leveraging large datasets to identify patterns and model-based methods rooted in physics-based interpretations for diagnosis. Both approaches offer complementary advantages, and recent developments in each are presented in Section 4.2.

Finally, the methodology surfaced essential key information that serves as a resource base for advancing research in PV fault diagnosis. This additional insight includes commonly monitored physical

variables (such as irradiance, voltage, and current), frequently detected fault types, and key open-access databases available for PV fault detection studies. These resources, detailed in Section 4.3, provide foundational support for researchers aiming to develop or refine machine learning approaches in this domain.

## 4.1. Topic categories in machine learning for photovoltaic fault diagnosis

The SL approach is the most widely utilized method in PV fault diagnosis, leveraging labeled data to classify various types of faults. These methods achieve high accuracy in controlled conditions, as they benefit from databases with inputs (predictors) paired with labeled outputs (targets) [44]. However, in PV applications, SL faces challenges related to seasonality and the large datasets needed for models to be generalizable. Algorithms in this category, such as Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), Probabilistic Neural Network (PNN), Extreme Learning Machine (ELM), Ensemble Learning (EL), Deep Learning (DL), SVM, and KNN, have demonstrated high performance in diagnosing issues like partial shading, open circuits, and degradation faults [45]. Notably, recent work has shown that deep Convolutional Neural Networks (CNN) can be optimized and embedded into low-cost hardware platforms, such as the Raspberry Pi, to enable real-time fault detection and classification using thermographic images [46].

SSL is especially useful when labeled data is limited, combining a small amount of labeled data with a large volume of unlabeled data to improve model robustness. This category proves effective in environments with varying conditions, as SSL algorithms like graph-based models, ladder networks, and positive unlabeled learning adaptively enhance diagnosis accuracy [47]. However, SSL methods may face limitations in fault localization under highly variable conditions [48].

RL is mainly applied in control-related PV diagnosis, where agents learn optimal actions based on environmental feedback. This continuous learning process allows RL models to improve over time, making them effective in dynamic PV systems. Nevertheless, RL algorithms often require substantial computational resources and have lengthy training times, which can be restrictive in real-time fault diagnosis applications [49]. RL applications in PV fault diagnosis cover complex control scenarios and the detection of recurrent or progressive faults [50].

UL is particularly relevant in scenarios lacking labeled data, such as anomaly detection and clustering applications in PV fault detection [51]. Algorithms in this category, including stacked autoencoders [52] and clustering techniques [53], autonomously detect patterns from unlabeled input data [54]. UL methods prove advantageous in identifying novel faults, but they often struggle with interpretability and
<table><caption>**Table 2**
Comparison of technical algorithm details found in the top five cited articles within Topics 1, including algorithm limitations.</caption><thead><tr><th>Topic</th><th>Ref</th><th>Testing Accuracy (%)</th><th>Sim</th><th>Real</th><th>Fault</th><th>Data</th><th>Software</th><th>Method</th><th>Limitation</th></tr></thead><tbody><tr><td rowspan="5">SL</td><td>[45]</td><td>99.88</td><td>✓</td><td>✓</td><td>Partial Shading, OC, SC, Degradation</td><td>4800</td><td>Matlab Simulink</td><td>Optimized kernel extreme learning machine</td><td>High computational cost</td></tr><tr><td>[56]</td><td>99.930 - 99.993</td><td>-</td><td>✓</td><td>LL, Shading, Degradation</td><td>1501</td><td>Matlab Simulink</td><td>Random forest</td><td>Limited generalization</td></tr><tr><td>[57]</td><td>100</td><td>✓</td><td>✓</td><td>Faulty conditions</td><td>182 160</td><td>Matlab Simulink and PSIM</td><td>PNN</td><td>Noise-sensitive</td></tr><tr><td>[58]</td><td>97.67</td><td>-</td><td>✓</td><td>SC, shading</td><td>-</td><td>Matlab</td><td>EL</td><td>Not ideal for real-time</td></tr><tr><td>[59]</td><td>95</td><td>-</td><td>✓</td><td>OC</td><td>1500</td><td>-</td><td>Vector machines, logistic regression, and random forest</td><td>Issues with non-linear faults</td></tr></tbody></table>

<table><caption>**Table 3**
Comparison of technical algorithm details found in the top five cited articles within Topics 2, including algorithm limitations.</caption><thead><tr><th>Topic</th><th>Ref</th><th>Testing Accuracy (%)</th><th>Sim</th><th>Real</th><th>Fault</th><th>Data</th><th>Software</th><th>Method</th><th>Limitation</th></tr></thead><tbody><tr><td rowspan="5">SSL</td><td>[60]</td><td>100</td><td>✓</td><td>-</td><td>LL, OC</td><td>4879</td><td>Matlab Simulink</td><td>Graph-based semi-supervised learning</td><td>Struggles with complex faults</td></tr><tr><td>[48]</td><td>100</td><td>✓</td><td>-</td><td>LL, OC</td><td>35 547</td><td>Matlab</td><td>Graph-based semi-supervised learning</td><td>Data-heavy</td></tr><tr><td>[61]</td><td>93.12</td><td>✓</td><td>-</td><td>different types of shading</td><td>15 000</td><td>-</td><td>Semi-supervised probabilistic machine learning</td><td>Low shading precision</td></tr><tr><td>[62]</td><td>90</td><td>✓</td><td>-</td><td>Shading, Soiling, Degradation, SC</td><td>85 940</td><td>PVWatts calculator</td><td>Positive Unlabeled Learning</td><td>Low classification accuracy</td></tr><tr><td>[63]</td><td>99.5</td><td>✓</td><td>-</td><td>Fault condition, shading</td><td>-</td><td>Matlab Simulink</td><td>N-semi-regular Fuzzy Semi-Supervised Learning</td><td>Sensitive to noise</td></tr></tbody></table>

<table><caption>**Table 4**
Comparison of technical algorithm details found in the top five cited articles within Topics 3, including algorithm limitations.</caption><thead><tr><th>Topic</th><th>Ref</th><th>Testing Accuracy (%)</th><th>Sim</th><th>Real</th><th>Fault</th><th>Data</th><th>Software</th><th>Method</th><th>Limitation</th></tr></thead><tbody><tr><td rowspan="6">RL</td><td>[64]</td><td>84.0-86.2</td><td>✓</td><td>-</td><td>Aging, Soiling, OC, SC, Faulty inverter</td><td>16 000</td><td>-</td><td>Deep Reinforcement Learning</td><td>Needs long training</td></tr><tr><td>[65]</td><td>84.0-96.6</td><td>-</td><td>✓</td><td>Fault condition</td><td>4000</td><td>-</td><td>Deep reinforcement learning</td><td>Hyperparameter tuning</td></tr><tr><td>[66]</td><td>97.6 - 98.9</td><td>✓</td><td>-</td><td>Short-circuit, open-circuit, degradation, shading</td><td>-</td><td>Matlab Simulink</td><td>Genetic algorithm and ANN</td><td>Computationally heavy</td></tr><tr><td>[67]</td><td>94 - 99</td><td>-</td><td>✓</td><td>Fault condition</td><td>Multiple databases</td><td>-</td><td>Deep Reinforcement Learning</td><td>Sensitive to dynamic conditions</td></tr><tr><td>[68]</td><td>95-100</td><td>✓</td><td>✓</td><td>Drive fault, SC, OC, failures of the power switching devices</td><td>1000</td><td>Matlab</td><td>Hidden Markov model and genetic algorithm</td><td>Complex for real-time</td></tr></tbody></table>

accuracy due to the absence of explicit labels. Despite these limitations, unsupervised models are increasingly valuable for identifying unknown fault patterns in evolving PV systems [55].

Tables 2–5 provide a detailed comparison of technical characteristics, including testing methodologies (simulated or real data), fault types addressed, required data volumes, and software tools utilized in each study. The table also outlines algorithmic approaches, limitations, and accuracy values derived from the top five cited articles within each learning category.

The results presented in this section demonstrate that SL achieves high accuracy under controlled conditions. However, it is limited by its dependence on labeled datasets and seasonally varying data, which can impact generalization. SSL provides a balance by requiring minimal labeled data but encounters challenges in fault localization within highly variable environments. RL shows promise in dynamic environments but is computationally intensive and often requires lengthy training periods, which may not be feasible for real-time diagnosis applications. UL is valuable for identifying new fault patterns in unlabeled datasets,
<table><caption>**Table 5**
Comparison of technical algorithm details found in the top five cited articles within Topics 4, including algorithm limitations.</caption><thead><tr><th>Topic</th><th>Ref</th><th>Testing Accuracy (%)</th><th>Sim</th><th>Real</th><th>Fault</th><th>Data</th><th>Software</th><th>Method</th><th>Limitation</th></tr></thead><tbody><tr><td rowspan="4">UL</td><td>[52]</td><td>97.12–98.56</td><td>✓</td><td>✓</td><td>SC, Shading, Degradation</td><td>2470</td><td>Matlab Simulink and PSIM Matlab Simulink</td><td>Stacked auto-encoder and clustering Bilayered ML scheme with weighted KNN Hierarchical anomaly detection</td><td>Low interpretability Limited adaptability High false positives</td></tr><tr><td>[54]</td><td>100</td><td>–</td><td>✓</td><td>Fault condition</td><td>–</td><td>Matlab</td><td>Sparse auto encoder</td><td>Noise sensitivity Limited clustering precision</td></tr><tr><td>[69]</td><td>78.8–90.2</td><td>✓</td><td>–</td><td>Shading, hotspot, aging, glass breakage</td><td>–</td><td>Matlab</td><td>Self-organizing map neural network</td><td>Noise sensitivity Limited clustering precision</td></tr><tr><td>[55]</td><td>98.55–100</td><td>✓</td><td>–</td><td>LG, LL</td><td>8058</td><td>OPAL-RT simulator</td><td>OPAL-RT simulator</td><td>Noise sensitivity Limited clustering precision</td></tr><tr><td></td><td>[70]</td><td>54.35–99.83</td><td>✓</td><td>✓</td><td>SC</td><td>51 139</td><td>Matlab Simulink</td><td>Matlab Simulink</td><td>Noise sensitivity Limited clustering precision</td></tr></tbody></table>

<table><caption>**Table 6**
General advantages and disadvantages of AI learning types applied to PV fault diagnosis.</caption><thead><tr><th>Learning type</th><th>Advantages</th><th>Disadvantages</th></tr></thead><tbody><tr><td>Supervised Learning (SL)</td><td><ul><li>High accuracy in controlled environments.</li><li>Well-established and widely used.</li><li>Suitable for clearly labeled datasets.</li></ul></td><td><ul><li>Requires large volumes of labeled data.</li><li>Struggles to generalize across varying environmental conditions.</li><li>Prone to overfitting if not tuned properly.</li></ul></td></tr><tr><td>Semi-Supervised Learning (SSL)</td><td><ul><li>Effective with limited labeled data.</li><li>Leverages large amounts of unlabeled data.</li><li>Reduces annotation costs significantly.</li></ul></td><td><ul><li>Can be sensitive to labeling errors.</li><li>Fault localization may be less precise.</li><li>Performance varies under high variability in real-world data.</li></ul></td></tr><tr><td>Reinforcement Learning (RL)</td><td><ul><li>Adapts well to dynamic and control-based environments.</li><li>Capable of learning optimal fault-handling policies over time.</li><li>Suitable for real-time decision-making tasks.</li></ul></td><td><ul><li>High computational cost and long training times.</li><li>Requires extensive simulations or real-time feedback.</li><li>Challenging to deploy in resource-constrained environments.</li></ul></td></tr><tr><td>Unsupervised Learning (UL)</td><td><ul><li>Useful for detecting novel or unknown fault types.</li><li>No need for labeled datasets.</li><li>Good for anomaly detection and clustering.</li></ul></td><td><ul><li>Lower interpretability and explainability.</li><li>Difficult to validate results without labels.</li><li>Potentially lower accuracy in classification tasks.</li></ul></td></tr></tbody></table>

though it often suffers from reduced interpretability and accuracy due to the absence of labeled data.

To complement the analysis presented in Tables 2–5, Table 6 summarizes the general advantages and disadvantages of each learning category (SL, SSL, RL, and UL) applied in PV fault diagnosis. This overview facilitates a clearer understanding of the main trade-offs and methodological considerations when selecting an appropriate algorithmic approach for different diagnostic contexts.

## 4.2. Recent developments in data-driven and model-based approaches

The Topic-tSNE Fusion methodology identified two prominent approaches across the analyzed topics in PV fault diagnosis: data-driven and model-based methods. These approaches offer distinct advantages and limitations, making each suitable for different diagnosis scenarios. Data-driven approaches rely on historical and real-time datasets to detect fault patterns and abnormalities, often through machine learning and statistical techniques [47]. In contrast, model-based approaches are grounded in physics-based models that simulate PV system behavior under different fault conditions, providing interpretability and reliability in data-scarce environments [64].

**Data-Driven Approaches:** Data-driven methods are employed to utilize historical and real-time data for identifying fault patterns and anomalies in PV systems. These approaches generally apply vast amounts of data and statistical learning algorithms to predict and diagnose faults without needing explicit models of the PV system. This approach proves advantageous for capturing complex fault patterns

as it adapts to evolving data [45]. However, data-driven models may necessitate extensive datasets for training, with predictive accuracy declining in data-scarce or highly variable conditions [59]. Algorithms such as neural networks, random forests, and support vector machines exemplify data-driven approaches in PV diagnosis [48].

Recent ensemble learning strategies combining boosting and bagging with Double Exponentially Weighted Moving Average (DEWMA) charts have achieved high sensitivity in identifying multiple PV anomalies using real-world data [71]. Similarly, exponential smoothing methods integrated with empirical models have demonstrated effectiveness in tracking subtle deviations such as shading or circuit faults [72]. Kernel-based approaches like support vector regression and Gaussian process regression have been applied for residual analysis and filtering, with Gaussian Process Regression (GPR) showing superior performance in PV fault scenarios [73]. Furthermore, semi-supervised deep learning using variational autoencoders has proven efficient at detecting inverter and controller faults using only normal operation data [74].

**Model-Based Approaches:** Model-based techniques, on the other hand, involve the use of physics-based or simulation models to interpret PV system behaviors. This approach is advantageous when data is limited or unavailable, relying on predefined models to simulate fault scenarios [49]. Model-based methods are generally more interpretable, although they may struggle to adapt to new or unmodeled faults due to their reliance on pre-established models. Examples include methods based on equivalent circuit models or physics-driven anomaly detection, widely applied in structured diagnosis settings [50].
<table><caption>**Table 7**
Comparison of technical algorithm details found in selected articles within Data-Based Approaches, including algorithm limitations.</caption><thead><tr><th></th><th>Ref</th><th>Accuracy (%)</th><th>Fault type</th><th>Data</th><th>Method</th><th>Limitation</th></tr></thead><tbody><tr><td rowspan="10">Data-Driven</td><td>[76]</td><td>88-89</td><td>Snail trail</td><td>16</td><td>Ensemble Learning</td><td>Tested on a single type of fault.</td></tr><tr><td>[77]</td><td>87.5</td><td>Snail trail, Broken Glass</td><td>12</td><td>PLS-LDA</td><td>Tested in a single weather condition</td></tr><tr><td>[78]</td><td>94-100</td><td>Anomaly detection</td><td>10</td><td>Ensemble-based machine learning</td><td>Requires extensive training data</td></tr><tr><td>[6]</td><td>72-97</td><td>Anomaly detection</td><td>8199/ 9451</td><td>Auto Threshold</td><td>Sensitivity to noise</td></tr><tr><td>[79]</td><td>-</td><td>Shading, Short Circuit</td><td>-</td><td>Fuzzy C-Means Clustering</td><td>Sensitivity to noise and environmental fluctuations</td></tr><tr><td>[80]</td><td>99.93</td><td>Symmetrical, Asymmetrical, ground faults</td><td>264</td><td>Long Short-Term Memory</td><td>High sensitivity to noise</td></tr><tr><td>[81]</td><td>96.95</td><td>Open Circuit, Short Circuit</td><td>3500</td><td>XGBoost</td><td>Sensitivity to noisy data</td></tr><tr><td>[9]</td><td>99.93</td><td>Short circuit, open circuit, degradation, partial shading</td><td>25 237</td><td>Deep residual network</td><td>High memory and computational demand</td></tr><tr><td>[82]</td><td>97.9</td><td>Disconnection, partial shading</td><td>97 200</td><td>Artificial Neural Network (RBF)</td><td>Limited generalization for diverse environmental conditions</td></tr><tr><td>[83]</td><td>94.5</td><td>UV degradation, oxidation</td><td>32 000</td><td>Generative Adversarial Network</td><td>Limited by image resolution, high computational demand</td></tr></tbody></table>

<table><caption>**Table 8**
Comparison of technical algorithm details found in selected articles within Model-Based Approaches, including algorithm limitations.</caption><thead><tr><th></th><th>Ref</th><th>Accuracy (%)</th><th>Fault type</th><th>Data</th><th>Method</th><th>Limitation</th></tr></thead><tbody><tr><td rowspan="10">Model-Based</td><td>[84]</td><td>-</td><td>Serial arc faults</td><td>12</td><td>Model-based estimation</td><td>Limited in adaptability for varying fault types</td></tr><tr><td>[85]</td><td>-</td><td>Fault tracking, detection, clearing, and rearrangement</td><td>-</td><td>ANFIS</td><td>High computational load required</td></tr><tr><td>[86]</td><td>89.51</td><td>Multiple failures</td><td>-</td><td>Kernel GLRT</td><td>Requires large memory and computational power</td></tr><tr><td>[87]</td><td>68.6</td><td>Bypass diode and mismatch faults</td><td>-</td><td>Gaussian process regression</td><td>Resource-intensive</td></tr><tr><td>[88]</td><td>83.2-99.9</td><td>I-V curve analysis</td><td>4560</td><td>Artificial Neural Networks</td><td>High computational demand</td></tr><tr><td>[89]</td><td>99.12</td><td>Line-line, line-ground, open-circuit faults</td><td>5625</td><td>1-D CNN and MLP</td><td>High memory requirement due to extensive simulation data processing</td></tr><tr><td>[90]</td><td>76-86</td><td>Short-circuit, inverse bypass diode, shunted bypass diode, shadowing effect</td><td>300</td><td>SVM with PCA</td><td>Less effective for classifying boundary points</td></tr><tr><td>[91]</td><td>97.32</td><td>Open-circuit, Short-circuit, Shading, Degradation</td><td>738</td><td>Fine-tuning Naive Bayesian Model</td><td>Cannot distinguish between short-circuit and severe shading faults</td></tr><tr><td>[92]</td><td>98-99</td><td>Open-circuit, shading, sensor faults</td><td>1501</td><td>Interval-valued Ensemble Learning (IEL)</td><td>High computational cost for large datasets</td></tr><tr><td>[93]</td><td>99</td><td>Tracker faults</td><td>340 000</td><td>Physics-informed deep learning</td><td>Requires precise model calibration and substantial data preprocessing</td></tr></tbody></table>

Hybrid strategies combining residual generation with KNN classifiers and monitoring charts such as Exponentially Weighted Moving Average (EWMA) have also been proposed, showing high detection performance for DC-side faults while being adaptable to nonlinear system characteristics [75].

This section outlines the main characteristics of each approach and highlights the algorithms most frequently utilized in recent studies. Tables 7 and 8 provide a comparative summary of ten notable and recent algorithms for both data-driven and model-based methods, detailing their primary applications, strengths, and limitations within PV fault diagnosis.

### 4.3. Key additional information for advancing photovoltaic fault diagnosis

Through the application of the Topic-tSNE Fusion methodology, several critical aspects were identified as essential for advancing fault

diagnosis in PV systems using artificial intelligence. Beyond the categorization of fault diagnosis methods, this approach highlighted specific physical variables, common fault types, and accessible databases that serve as invaluable resources in this field.

In this section, three key areas of additional information are detailed: (1) commonly used physical variables, (2) frequently detected faults, and (3) open-access databases that support further research.

**Commonly Used Physical Variables:** Essential variables for PV fault diagnosis include irradiance, voltage, and current, as these directly impact PV module performance. By monitoring these parameters, anomalies indicating potential faults can be detected. Furthermore, advanced imaging techniques such as thermal and electroluminescence imaging are crucial for identifying temperature-related issues and structural defects within PV modules, as shown in Fig. 7.
<figure><img src="image_10.png" /><figcaption>Fig. 7. Typical variables used for fault diagnosis.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 8. Typical faults studied in the state of the art.</figcaption></figure>

**Frequently Detected Faults:** Common faults in PV systems encompass arc faults, open circuits, short circuits, degradation, and line-to-line faults. Each type of fault exhibits distinct characteristics and impacts the overall performance and efficiency of PV systems. For instance, arc faults can cause significant overheating, while degradation affects module efficiency over time. Fig. 8 illustrates the various faults typically studied in PV fault diagnosis research.

**Open Databases for Research:** Three main types of open-access databases were identified, categorized by the data they provide: electrical, image-based, and additional resources. Electrical databases often include irradiance, temperature, and shading data, which are integral for both real-time fault detection and historical analysis. Image-based databases offer data from thermal and electroluminescence imaging, supporting visual inspection and defect identification. Other databases, such as those for remote sensing and solar irradiation data, provide valuable contextual information. Table 9 summarizes these open-access databases, each offering diverse data types that facilitate comprehensive fault diagnosis research.

# 5. Discussion

The *Topic-tSNE Fusion* methodology applied in this study provides a structured and scalable framework for analyzing the landscape of fault diagnosis in PV systems. By integrating natural language processing, probabilistic topic modeling, and high-dimensional data visualization, the approach enables the objective synthesis of large scientific corpora. Through the analysis of 983 articles, four primary thematic

clusters were identified, SL, SSL, RL, and UL, each representing distinct algorithmic strategies used in the field.

This section first compares the proposed methodology against conventional literature review techniques, highlighting its advantages in terms of objectivity, scalability, and reproducibility. It then explores current research trends, challenges, and future directions identified across the analyzed state of the art.

## 5.1. Comparison with conventional review methods

To highlight the advantages of the proposed *Topic-tSNE Fusion* methodology compared to traditional state of the art review approaches, Table 10 provides a comparative analysis across key aspects such as scalability, objectivity, topic detection, and reproducibility. This comparison demonstrates the improved robustness and reduced subjectivity offered by the *Topic-tSNE Fusion* approach in synthesizing large scientific corpora.

## 5.2. International perspectives on PV fault diagnosis

The methodological framework proposed in this study offers a broad and inclusive perspective on photovoltaic fault diagnosis by synthesizing literature from a wide range of countries and research contexts. The dataset of 983 documents was compiled from major bibliographic databases, Scopus, Web of Science, IEEE Xplore, ScienceDirect, and SpringerLink, incorporating contributions from Europe, Asia, the Americas, and the Middle East. This geographic diversity ensures that the thematic clusters identified in the analysis capture both region-specific challenges and widely shared concerns, such as environmental variability, data scarcity, and implementation constraints in real-world systems.

For instance, the growing interest in edge computing for rural PV systems appears in studies from both industrialized and developing nations. Likewise, the need to address limited labeled data using SSL is evident in research spanning countries such as India, Brazil, and several African nations. These findings enhance the generalizability of the study and demonstrate that the proposed methodology is well suited for analyzing and comparing research across global contexts in AI-driven PV fault diagnosis.

## 5.3. Promising research topics and challenges

The analysis highlights several promising research directions and ongoing challenges, underscoring the potential of hybrid machine learning techniques and the application of less commonly used algorithms in PV fault diagnosis.

### 5.3.1. Key trends and hidden relationships

A comprehensive review of 983 articles, enhanced by the application of the *Topic-tSNE Fusion* methodology, revealed both explicit research trends and previously hidden semantic relationships. This combined approach allowed for a clearer understanding of well-established focal points in the field, while simultaneously surfacing emerging directions that were not apparent in traditional reviews.

Among the most prominent trends observed:

* A predominant focus on SL techniques, especially neural networks and support vector machines.

* Growing interest in semi-supervised and RL for improving diagnosis robustness in dynamic environments.

* Increasing use of hybrid models combining data-driven and model-based approaches.

* Emphasis on critical variables such as irradiance, voltage, and current for accurate diagnosis.

* A shift from small, controlled datasets toward the use of open-access, real-world PV data repositories.
<table><caption>**Table 9**
Summary of databases in the photovoltaic domain aimed at fault detection.</caption><thead><tr><th>Database type</th><th>Ref</th></tr></thead><tbody><tr><td><strong>Electrical</strong></td><td></td></tr><tr><td>PV Panel: Irradiance, Temperature, Partial Shading - IV Curves</td><td>[94]</td></tr><tr><td>Fault Detection Dataset in Photovoltaic Farms</td><td>[95]</td></tr><tr><td>DKASC Production Databases</td><td>[96]</td></tr><tr><td>Pecan Street Dataport</td><td>[97]</td></tr><tr><td>Operational Data from a Grid-Connected Photovoltaic Plant</td><td>[98]</td></tr><tr><td>The GPVS-Faults Database</td><td>[99]</td></tr><tr><td>Partial Shading in a Photovoltaic Module</td><td>[100]</td></tr><tr><td><strong>Image</strong></td><td></td></tr><tr><td>Electrical Behavior of Photovoltaic Panels from RGB Images</td><td>[101]</td></tr><tr><td>Photovoltaic Thermal Images Dataset</td><td>[102]</td></tr><tr><td>Snail Trails and Hot Spot Failures in Monocrystalline Si Solar Panels</td><td>[103]</td></tr><tr><td>Defective Solar Cells in Electroluminescence Imagery</td><td>[104]</td></tr><tr><td>Defect Detection in Electroluminescence Images of Solar Cells</td><td>[105]</td></tr><tr><td><strong>Other Potential Databases</strong></td><td></td></tr><tr><td>Dataset for Remote Sensing Object Identification</td><td>[106]</td></tr><tr><td>Solar Irradiation Data</td><td>[107]</td></tr><tr><td>The National Solar Radiation Data Base (NSRDB)</td><td>[108]</td></tr><tr><td>Multi-Resolution Dataset for Photovoltaic Panel Segmentation</td><td>[109]</td></tr><tr><td>SPVPANELEX</td><td>[110]</td></tr><tr><td>MATLAB/Simulink Model of Photovoltaic Cell, Panel, and Array</td><td>[111]</td></tr><tr><td>Piedmont Photovoltaic Panels Dataset</td><td>[112]</td></tr></tbody></table>

In addition to these known patterns, the Topic-tSNE Fusion analysis uncovered new thematic linkages and underexplored areas, as discussed in the following subsection.

### 5.3.2. Emerging semantic patterns revealed by Topic-tSNE fusion

A central contribution of the semantic analysis was its ability to uncover relationships between research themes that are not easily identified through manual or keyword-based reviews. For instance, it revealed a strong connection between SSL algorithms and fault types such as partial shading and soiling—links that had not been emphasized in earlier works.

The algorithm also identified an emerging focus on fault types like snail trails, which were traditionally detectable only via imaging techniques. Recent studies suggest that such defects are now being detected through electrical signal analysis, extending diagnostic capabilities beyond visual methods.

Moreover, the semantic landscape indicated a growing emphasis on frugal AI, numerically sober models, and durable architectures for real-world scenarios. These are closely associated with the expanding role of Edge AI and Cloud AI, where constraints like latency, hardware limitations, and energy usage shape system design.

Other clustered themes pointed to a convergence of model-based reasoning with deep learning for anomaly detection, as well as RL techniques coupled with thermal imaging for real-time control—indicating increasing cross-domain integration.

These insights illustrate the added value of dimensionality-reduced semantic mapping in capturing latent trends and revealing promising directions in PV fault diagnosis.

The following subsections further explore these directions, organized by methodological paradigm, deployment challenge, and system-level trade-offs.

### 5.3.3. Hybrid techniques and algorithmic expansion

Hybrid machine learning techniques in PV fault diagnosis are increasingly popular, as combining multiple algorithmic approaches can improve diagnosis accuracy and robustness [113]. For example, hybrid models that combine ANN with fuzzy logic systems leverage strengths across different algorithmic families, enhancing fault detection capabilities under varying operational and environmental conditions [77].

Expanding the scope of algorithms within SL could further refine fault diagnosis. Underutilized algorithms like KNN, Adaptive Boosting, GPR, and ElasticNet offer potential for addressing challenges such as seasonally variable fault patterns and noise-prone datasets [114].

### 5.3.4. Semi-supervised learning applications and future potential

SSL addresses data scarcity, particularly in labeled datasets, which is a common limitation in PV diagnosis. SSL techniques, such as graph-based SSL and probabilistic modeling, facilitate the management of large-scale unlabeled datasets, reducing dependency on manual labeling. Approaches like positive unlabeled learning and fuzzy SSL enable structured continuous learning in dynamic environments [60]. Future research could explore integrating SSL with transfer learning or domain adaptation to enhance model adaptability across different PV plant conditions.

### 5.3.5. Advances in reinforcement learning and control optimization

RL has demonstrated substantial potential, especially in areas like autonomous plant control and adaptive maintenance scheduling. RL frameworks, including Q-Learning and Deep Q-Networks (DQNs), enable adaptability in non-linear environments typical of PV systems, where preventing control failures is crucial [49]. Further investigation into RL techniques such as policy optimization and temporal difference learning could improve robustness and flexibility in managing control tasks within PV systems.
<table><caption>**Table 10**
Comparison between conventional state of the art reviews and the Topic-tSNE Fusion methodology.</caption><thead><tr><td>Aspect</td><td>Conventional reviews</td><td>Topic-tSNE fusion</td></tr></thead><tbody><tr><td>Scope</td><td>Limited to tens or hundreds of articles manually reviewed</td><td>Scalable to hundreds or thousands of documents</td></tr><tr><td>Objectivity</td><td>Prone to selection bias and subjective interpretation</td><td>Based on data-driven algorithms and probabilistic modeling</td></tr><tr><td>Topic Detection</td><td>Requires expert identification and manual classification</td><td>Automated topic extraction using LDA and clustering</td></tr><tr><td>Visualization</td><td>Rarely includes high-dimensional analysis or clustering visuals</td><td>Incorporates t-SNE to reveal semantic relationships</td></tr><tr><td>Reproducibility</td><td>Difficult to replicate due to subjective criteria</td><td>Easily replicable with defined data and code pipelines</td></tr><tr><td>Granularity</td><td>Focuses on broad themes</td><td>Enables fine-grained topic and subtopic distinctions</td></tr><tr><td>Expert Involvement</td><td>Central to all stages</td><td>Focused on qualitative validation, not selection or classification</td></tr><tr><td>Hidden Trends</td><td>Often missed due to manual processing</td><td>Capable of surfacing latent patterns and co-occurrences</td></tr></tbody></table>

### 5.3.6. Opportunities and challenges in unsupervised learning

UL techniques offer valuable applications in fault diagnosis for PV systems, particularly in anomaly detection and clustering within unlabeled data. Despite challenges related to cluster interpretability, methods like fuzzy C-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and self-organizing maps demonstrate potential for handling high-dimensional data with minimal supervision [115]. Deep learning-based unsupervised methods, including autoencoders and generative models, could advance preventive diagnosis by revealing complex fault patterns in PV operational data.

### 5.3.7. Practical challenges and integration strategies in real-world deployment

The real-world deployment of AI-based fault detection systems in PV installations involves several practical challenges and architectural decisions, ranging from environmental variability and data issues to infrastructure limitations and integration complexities.

A major challenge is the variability in environmental conditions, such as changes in irradiance, seasonal variations, and partial shading. These factors reduce the ability of models trained in controlled settings to generalize effectively [93]. Furthermore, the scarcity of labeled fault data complicates SL approaches, although synthetic datasets have been proposed as a workaround [93].

From an infrastructure perspective, edge-based AI deployment offers benefits like low-latency inference and data privacy but is limited by hardware constraints [116]. Cloud-based models provide higher processing capacity and ease of updates but may suffer from bandwidth limitations and increased latency, particularly in remote PV farms [117].

Integration strategies for AI in PV monitoring systems must address these issues through appropriate deployment architectures. One approach is *Edge Integration*, where lightweight models such as decision trees or shallow neural networks are embedded on devices like inverters or microcontrollers. This allows real-time response and independence from external networks, making it ideal for rural or isolated systems [118].

Alternatively, *Cloud-Based Platforms* support more computationally intensive models like CNN or ensemble methods. In these setups, operational data is sent to cloud servers for processing, with results delivered to operators through web dashboards [119]. Moreover, recent studies emphasize the integration of artificial intelligence and Internet of Things (IoT) technologies to enhance remote sensing and fault diagnosis in photovoltaic systems. These AI-IoT frameworks show great

promise for improving monitoring efficiency and diagnostic capabilities in remote solar farms, where conventional maintenance and data collection remain challenging [120].

A promising solution is the adoption of *Hybrid Architectures* that combine edge and cloud advantages. For example, fast preliminary classification can be performed on-site, while detailed diagnostics and retraining are handled in the cloud. This balance enables low-latency detection and system-wide learning [121].

To make such architectures viable, support for industrial protocols like Modular Digital Communication Protocol (Modbus) or Open Platform Communications Unified Architecture (OPC UA) is essential for seamless communication with Supervisory Control and Data Acquisition (SCADA) systems. AI frameworks like TensorFlow Lite and Open Neural Network Exchange (ONNX) are useful for deploying models in resource-constrained environments [122].

Interpretability is another deployment requirement. Explainable AI tools such as SHapley Additive exPlanations (SHAP) can help maintenance teams understand model outputs, particularly in complex fault scenarios [123]. Additionally, using AI-powered anomaly detection as a pre-filter can reduce noise and computational load for downstream models [124].

Challenges also arise from real-world signal degradation and data loss, necessitating robustness in model design [78]. Furthermore, understanding the severity or energy impact of faults remains a complex but important task [7].

Data acquisition strategies must balance resolution with feasibility. High-frequency sampling is needed for transient faults like arcing [125], while long-term issues like degradation may only require sparse data. The heterogeneity of PV system layouts further complicates model reuse across sites, requiring normalization or domain adaptation [87].

Temporal changes, such as system aging, can shift performance baselines. Without continuous model updates, normal degradation might be misclassified as a fault [59]. Therefore, dynamic model adaptation is crucial.

Overall, successful deployment requires a tailored strategy considering model complexity, hardware capabilities, bandwidth, and the evolving behavior of PV systems. Integrating these elements ensures scalable, interpretable, and efficient AI-driven fault detection in operational PV environments.

### 5.3.8. Comparative evaluation of complexity, cost, and efficiency

Evaluating fault detection methods for real-world deployment requires consideration of three key factors: computational complexity,
implementation cost, and operational efficiency. SL methods (e.g., SVM, ANN) are widely used due to their high classification accuracy; however, they often require large, labeled datasets and significant computing resources for training and inference [126]. This makes them complex and potentially costly to deploy, particularly on edge devices with limited processing power.

Unsupervised approaches, such as clustering or anomaly detection, offer a more lightweight alternative and are generally more cost-effective to deploy, especially when labeled data is scarce. For instance, Chokr et al. [127] demonstrated the potential of clustering techniques for detecting PV faults with minimal supervision. Similarly, Hajji et al. [128] showed that unsupervised anomaly detection methods can identify abnormal behavior in PV systems using unlabeled datasets. However, these methods may suffer from reduced accuracy and interpretability, which can affect diagnostic efficiency in operational contexts.

SSL represents a middle ground by leveraging unlabeled data to reduce annotation costs, though the models can still be sensitive to noise and require careful tuning [78]. RL techniques are particularly powerful for adaptive control, yet they remain computationally intensive and are challenging to implement in resource-constrained or latency-sensitive environments [117].

From an infrastructure standpoint, edge-based implementations lower operational cost and latency but are constrained in model complexity. In contrast, cloud-based methods support more complex models and frequent updates but incur higher energy and communication costs. Hybrid architectures can balance these trade-offs, achieving acceptable accuracy and response times with manageable cost and complexity.

Overall, no single method dominates across all metrics; the choice depends on the specific application, available infrastructure, and diagnostic requirements.

## 5.4. Future work

This section outlines two primary directions for future research: (i) improvements to the Topic-tSNE Fusion methodology used for large-scale literature reviews; and (ii) novel developments in the application of artificial intelligence to fault diagnosis and control in photovoltaic systems.

### 5.4.1. Enhancing the Topic-tSNE fusion review methodology

While the current study applies Topic-tSNE Fusion within the context of PV fault diagnosis, the method is designed to be domain-agnostic. Its structured, data-driven architecture, based on widely used machine learning components, makes it a potentially effective tool for other research-intensive domains. Although empirical testing in external fields remains a task for future work, the conceptual adaptability of the framework and the transparency of the methodology suggest strong prospects for cross-domain application.

Future research could explore its applicability in other PV-related domains, such as energy forecasting, control strategies, or system sizing. Beyond photovoltaics, the methodology could be applied to complex domains where state of the art is rapidly growing, such as climate change, medical diagnostics, smart grids, and Industry 4.0. In these contexts, Topic-tSNE Fusion can provide a scalable and objective framework for identifying hidden thematic structures and synthesizing scientific knowledge.

Further improvement of the method could involve integrating bibliometric analysis to study temporal evolution and research trends, as well as categorizing documents by data type (e.g., I-V curves, thermal images, or production data) to better understand algorithm applicability per fault context.

An interesting complementary direction is the integration of bibliometric techniques with the current methodology to enhance the geographic understanding of research on PV fault diagnosis. For instance, the study by [11] already applies bibliometric and expert

analysis in the context of PV fault diagnosis, uncovering regional publication patterns and research trends. Building on this, future work could explore the integration of such bibliometric approaches with the Topic-tSNE Fusion framework presented in this study. This could provide a more comprehensive understanding of how regional factors – such as climate, economic development, or technological infrastructure – shape diagnostic strategies. Nevertheless, it is important to recognize that geographic metadata is often incomplete or inconsistently structured across databases. In such cases, full-text analysis or generative AI models capable of inferring institutional or geographic information may be required, making this a distinct and valuable line of future research.

Another relevant line of research is the development of hybrid validation mechanisms. Since the thematic clusters currently require expert judgment, combining coherence scores with external taxonomies or using crowdsourced expert evaluation may help reduce subjectivity and enhance reproducibility.

Although LDA was selected as the primary topic modeling algorithm in this study due to its balance between interpretability, maturity, and widespread adoption in state of the art analysis [30], future work will explore alternative techniques such as BERTopic, NMF, and STM. These methods offer different advantages. For example, BERTopic incorporates transformer-based embeddings for enhanced semantic coherence [129], while STM allows the incorporation of document-level metadata such as publication year or source [35]. A comparative evaluation of these methods could provide complementary perspectives on thematic structuring and further validate the robustness of the findings presented here.

Beyond selecting topic models, a promising direction is to explore the semantic relationships between topics themselves. Metrics such as cosine similarity, Jensen-Shannon divergence, or embedding-based comparisons could reveal thematic overlaps, complementarities, or potential causal associations. These techniques have been shown to align well with human assessments of topic similarity [130], support the aggregation of related topics to improve coherence [131], and offer scalable insights in large corpora [132]. This analysis could provide deeper insights into how diagnostic methods, failure types, and data modalities interact across the literature landscape.

An additional avenue for enhancement is the use of alternative dimensionality reduction algorithms in the visualization stage. Although TSNE was selected for its interpretability and visual clarity in cluster separation, recent advances suggest that Uniform Manifold Approximation and Projection (UMAP) could offer improved performance in preserving both local and global structure in high-dimensional data. UMAP has also been shown to provide faster computation and greater stability across runs due to its deterministic nature, which can be advantageous for large-scale scientific reviews [133]. Future versions of the methodology could incorporate a systematic comparison of UMAP and TSNE in terms of topic coherence alignment, runtime efficiency, and visualization fidelity, enhancing the robustness of the semantic mapping process.

### 5.4.2. Advancing AI applications in photovoltaic fault diagnosis

Several promising research directions can enhance the use of artificial intelligence in real-world PV diagnostic systems. First, the integration of Large Language Models (LLMs), such as GPT-4, Mistral AI, DeepSeek, etc, could improve automatic fault labeling using contextual information from monitoring logs or maintenance reports, and also assist in feature extraction from heterogeneous data.

Future models should also address data scarcity and heterogeneity across PV installations. This could be achieved by combining SSL with transfer learning or domain adaptation, enabling robust model generalization across different system configurations and aging profiles.

Adaptive machine learning frameworks that evolve over time are also needed to differentiate between natural system degradation and early-stage faults. These systems would recalibrate fault baselines using
longitudinal data and improve reliability under changing operating conditions.

Another key research area involves designing multiresolution sampling strategies that adjust the data acquisition frequency based on the type of fault-e.g., high-frequency sampling for arc faults and low-frequency for gradual degradation. This would reduce data load while preserving diagnostic accuracy.

Anomaly and outlier detection algorithms can be used as a first filter in AI pipelines, especially when labeled data is scarce. These methods can detect rare or novel fault conditions early and clean training datasets to reduce model bias and improve robustness.

In the context of *digital sobriety* and sustainable digital practices, future work should explore implementing AI models directly at the edge (e.g., on-site embedded systems). These models would perform real-time diagnostics with minimal latency and power consumption, while reducing reliance on cloud resources.

Intelligent monitoring systems specifically designed for diagnostic purposes also represent a key area. These systems should incorporate fault-specific data fusion (e.g., thermal + electrical signals), noise filtering, and autonomous event triggering for fault detection and classification.

Agent-based architectures could enable distributed diagnostic intelligence across PV components. Intelligent agents embedded at the panel, string, or inverter level could coordinate actions such as anomaly detection, reconfiguration requests, or triggering preventive maintenance routines. In future systems, this decentralized structure could be integrated with predictive control frameworks, enabling self-adaptive responses to identified fault conditions without centralized processing.

Finally, future research should prioritize the development of explainable and adaptive AI models tailored to edge, cloud, or hybrid environments. These systems must be capable of operating across heterogeneous PV configurations while addressing key deployment challenges such as interoperability, computational trade-offs, and evolving system behavior. By focusing on low-cost, scalable, and interpretable solutions, such models can enable robust and context-aware fault diagnosis across diverse monitoring infrastructures.

## 5.5. Limitations

While the *Topic-tSNE Fusion* approach offers a structured framework for categorizing and analyzing extensive research data, several limitations should be considered to provide a balanced perspective. First, the reliance on specific search criteria may have introduced biases, potentially excluding relevant studies outside the selected keywords or databases. Expanding search terms and incorporating additional databases may mitigate this limitation in future analyses.

Another limitation is the exclusion of non-English documents and general overview papers, which may contain valuable insights, particularly from regions where PV deployment is extensive. Although expert-sourced documents were included to enhance dataset comprehensiveness, this selection could have unintentionally skewed results, emphasizing certain algorithms or fault types over others. Additionally, while the dataset includes publications from multiple global databases, the lack of structured geographic metadata limits the ability to assess regional differences in diagnostic approaches.

Moreover, the validation and interpretability of the clusters currently rely on expert judgment during the qualitative analysis phase. While this ensures domain relevance, it introduces a degree of subjectivity and limits reproducibility. This reliance on human interpretation is an inherent limitation of topic modeling-based reviews. To address this, future studies should consider incorporating semi-automated or crowdsourced expert evaluations to reinforce consistency and transparency in thematic interpretations.

Furthermore, the reliance of the *tsne* algorithm on hyperparameters, such as perplexity, can affect cluster representation. Future studies may explore alternative dimensionality reduction techniques, such as

UMAP, to test for robustness across different visualization methodologies.

Finally, in this study, TSNE was employed primarily as a visual support tool to explore semantic structures in the topic modeling output. As such, hyperparameters like perplexity and learning rate were set using reference values from state of the art to achieve visually distinct cluster separation but were not systematically optimized. Although this choice served the visualization goals, it is important to note that these parameters can significantly influence the final layout of TSNE maps. In particular, research has shown that perplexity governs the level of local versus global structure in the output, often without preserving true distances, and that improper selection can lead to misleading visual clusters [134]. This reliance on fixed hyperparameters, rather than systematic tuning, introduces a potential source of bias in the interpretability of the resulting topic map.

## 6. Conclusions

This study presented a systematic, data-driven review of AI applications in PV fault detection, addressing the limitations of traditional state of the art reviews by developing and applying the *Topic-tSNE Fusion* methodology. Through the analysis of 983 articles, this innovative approach enabled the identification of four main thematic clusters within machine learning, SL, SSL, RL, and UL, each associated with specific strengths and applications in PV fault diagnosis. These machine learning approaches demonstrate varied diagnosis capabilities, with SL offering high accuracy in controlled conditions and RL excelling in control and dynamic PV environments. Moreover, unlike traditional bibliometric techniques such as co-citation or keyword co-occurrence analysis, which emphasize structural relationships between publications, the proposed approach focuses on contextual and semantic interpretation. As such, *Topic-tSNE Fusion* complements existing bibliometric methods by enriching trend analysis with topic-level granularity and interpretability.

A key contribution of this study is the objective classification of PV fault detection methodologies, distinguishing data-driven and model-based approaches. Data-driven approaches utilize historical and real-time datasets to recognize fault patterns, while model-based approaches rely on physics-based models that simulate PV system behavior under fault conditions. This categorization enhances the contextual understanding of diagnosis strategies across the identified machine learning approaches, allowing for a clearer view of diagnosis applications and revealing semantic relationships in the state of the art.

The analysis highlighted the potential of hybrid techniques and lesser-utilized algorithms within SL to improve diagnosis accuracy, as well as the adaptability of SSL in data-scarce environments. RL was shown to be particularly effective in control-related PV applications, while UL exhibited utility in anomaly detection and clustering tasks where labeled data are limited. The study further identified key physical variables commonly used in PV diagnosis, as well as primary fault types and open-access databases, which provide essential resources for developing advanced diagnosis frameworks.

The *Topic-tSNE Fusion* methodology offers a scalable, structured approach for systematically analyzing and categorizing the state of the art on AI-based PV fault diagnosis. This innovative framework facilitates a more objective examination of large datasets and serves as a foundational tool for advancing PV system diagnosis. The insights obtained provide valuable directions for optimizing machine learning applications within both data-driven and model-based frameworks in the renewable energy sector.

It is also important to emphasize that the applicability of different machine learning paradigms – such as supervised, semi-supervised, unsupervised, or reinforcement learning – depends not solely on the type of photovoltaic system (e.g., centralized, distributed, or residential), but primarily on the characteristics of the available data, the operational context, and the specific diagnostic objectives. As this work
is a systematic review, it does not aim to prescribe algorithmic choices for each system configuration. Instead, it offers a comprehensive thematic mapping that can support future studies in identifying suitable approaches based on contextual requirements and data constraints.

Although this study focused exclusively on photovoltaic fault diagnosis, the modular design of the *Topic-tSNE Fusion* methodology, combining topic modeling, dimensionality reduction, and expert-guided qualitative analysis, suggests promising applicability to other complex domains where large-scale, heterogeneous scientific literature exists. Fields such as medical diagnostics, industrial monitoring, or climate research could benefit from this structured and scalable approach. While its use outside the PV domain has not yet been tested, this transferability represents a valuable direction for future exploration.

## Acronyms

**DBSCAN** Density-Based Spatial Clustering of Applications with Noise

**DQNs** Deep Q-Networks

**GPR** Gaussian Process Regression

**KL** Kullback-Leibler

**SNE** Stochastic Neighbor Embedding

**tSNE** t-distributed stochastic neighbor embedding

**AI** Artificial Intelligence

**ANN** Artificial Neural Network

**BERTopic** Bidirectional Encoder Representations Topic Modeling

**CNN** Convolutional Neural Networks

**DEWMA** Double Exponentially Weighted Moving Average

**DL** Deep Learning

**DT** Decision Tree

**EL** Ensemble Learning

**ELM** Extreme Learning Machine

**EWMA** Exponentially Weighted Moving Average

**IoT** Internet of Things

**KNN** k-nearest neighbor

**LDA** Latent Dirichlet Allocation

**LLMs** Large Language Models

**ML** Machine Learning

**Modbus** Modular Digital Communication Protocol

**NB** Naïve Bayes

**NLTK** Natural Language Toolkit

**NMF** Non-negative Matrix Factorization

**ONNX** Open Neural Network Exchange

**OPC UA** Open Platform Communications Unified Architecture

**PNN** Probabilistic Neural Network

**PRISMA** Preferred Reporting Items for Systematic Reviews and Meta-Analyses

**PV** photovoltaic

**RF** Random Forest

**RL** Reinforcement Learning

**SCADA** Supervisory Control and Data Acquisition

**SHAP** SHapley Additive exPlanations

**SL** Supervised Learning

**SSL** Semi-Supervised Learning

**STM** Structural Topic Modeling

**SVM** Support Vector Machine

**UL** Unsupervised Learning

**UMAP** Uniform Manifold Approximation and Projection

## 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.
**Louise Travé-Massuyès:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
**Audine Subias:** Writing – original draft, Validation, Supervision.
**Marko Pavlov:** Validation, Resources.
**Corinne Alonso:** Writing – original draft, Validation, Supervision, Funding acquisition, Formal analysis.

## 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 work has benefited from the Artificial and Natural Intelligence Toulouse Institute ANITI funded by the France 2030 program under the Grant agreements n°ANR-19-PI3A-0004 and n°ANR-23-IACL-0002. It is also related to the SticAmSud project HAMADI 4.0 "Hybrid Algorithms based on Models and Data in Industry 4.0", n°22-STIC-06.

## Data availability

Data will be made available on request.

## References

[1] Jean J, Brown PR, Jaffe RL, Buonassisi T, Bulović V. Pathways for solar photovoltaics. Energy Env Sci 2015;8:1200–19.

[2] Araneo R, Lammens S, Grossi M, Bertone S. EMC issues in high-power grid-connected photovoltaic plants. IEEE Trans Electragn Compat 2009;51(3):639–48.

[3] Dhimish M, Holmes V. Fault detection algorithm for grid-connected photovoltaic plants. Sol Energy 2016;137:236–45.

[4] Hariharan R, Chakkarapani M, Saravana Ilango G, Nagamani C. A method to detect photovoltaic array faults and partial shading in pv systems. IEEE J Photovolt 2016;6(5):1278–85.
[5] Kurukuru VSB, Haque A, Khan MA, Tripathy AK. Fault classification for photovoltaic modules using thermography and machine learning techniques. In: 2019 international conference on computer and information sciences. 2019. p. 1–6.

[6] Zhao Y, Li D, Lu T, Lv Q, Gu N, Shang L. Collaborative fault detection for large-scale photovoltaic systems. IEEE Trans Sustain Energy 2020;11(4):2745–54.

[7] Boubaker S, Kamel S, Ghazouani N, Mellit A. Assessment of machine and deep learning approaches for fault diagnosis in photovoltaic systems using infrared thermography. Remote Sens 2023;15(6).

[8] Attouri K, Hajji M, Mansouri M, Harkat M-F, Kouadri A, Nounou H, et al. Fault detection in photovoltaic systems using machine learning technique. In: 2020 17th international multi-conference on systems, signals and devices. 2020. p. 207–12.

[9] Chen Z, Chen Y, Wu L, Cheng S, Lin P. Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Convers Manag 2019;198:111793.

[10] Mellit A, Tina GM, Kalogirou SA. Fault detection and diagnosis methods for photovoltaic systems: A review. Renew Sustain Energy Rev 2018;91:1–17.

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

[12] Kumaradurai A, Teekaraman Y, Coosemans T, Messagie M. Fault detection in photovoltaic systems using machine learning algorithms: A review. In: 2020 8th international conference on orange technology. Daegu, Korea (South): IEEE; 2020. p. 1–5.

[13] Et-taleby A, Chaibi Y, Benslimane M, Boussetta M. Applications of machine learning algorithms for photovoltaic fault detection: A review. Stat Optim Inf Comput 2023;11(1):168–77.

[14] Shen Y, Ji L, Xie Y, Huang G, Li X, Huang L. Research landscape and hot topics of rooftop PV: A bibliometric and network analysis. Energy Build 2021;251:111333.

[15] Mustak M, Salminen J, c Plé L, Wirtz J. Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. J Bus Res 2021;124:389–404.

[16] Chen X, Zou D, Cheng G, Xie H. Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of computers & education. Comput Educ 2020;151:103855.

[17] Boye Asmussen C, Møller C. Smart literature review: a practical topic modelling approach to exploratory literature review. J Big Data 2019;6:1–18.

[18] Berasategi A, Arostegi A. new optimized electrical architectures of a photovoltaic generators with high conversion efficiency [Ph.D. thesis], Université Paul Sabatier - Toulouse III; 2013.

[19] Sarr O. Analysis of failure modes effect and criticality analysis (FMECA): A stand-alone photovoltaic system. Sci J Energy Eng 2017;5:40.

[20] Jordan DC, Silverman TJ, Wohlgemuth JH, Kurtz SR, VanSant KT. Photovoltaic failure and degradation modes. Prog Photovolt: Res Appl 2017;25(4):318–26.

[21] Sepúlveda-Oviedo EH, Travé-Massuyès L, Subias A, Pavlov M, Alonso C. Detection and classification of faults aimed at preventive maintenance of PV systems. In: XI Congreso Internacional de Ingeniería Mecánica, Mecatrónica y Automatización 2023. Cartagena, Colombia: Universidad Nacional de Colombia; 2023. p. 1–2.

[22] Sepúlveda-Oviedo EH. A review of operational factors affecting photovoltaic system performance. Energy Convers Manag X 2025;26:100942.

[23] Sepúlveda-Oviedo EH. Impact of environmental factors on photovoltaic system performance degradation. Energy Strat Rev 2025;59:101682.

[24] Page MJ, McKenzie JE, Bossuyt P, Grimshaw JM, Hróbjartsson A, Lalu MM, et al. The prisma 2020 statement: An updated guideline for reporting systematic reviews. Syst Rev 2021;10(89).

[25] Wang J, Lim MK, Wang C, Tseng M-L. The evolution of the internet of things (IoT) over the past 20 years. Comput Ind Eng Management 2015;155:107174.

[26] Rakshikar N. Zotero: an ultimate citation management tool for researchers and academicians. In: UGC sponsored two day's national seminar on emerging trends in library technology. 2015. p. 1–6.

[27] Saheb T, Dehghani M. Artificial intelligence for sustainable energy: A contextual topic modeling and content analysis. 2022, ArXiv, abs/2110.00828.

[28] Jiang H, Qiang M, Lin P. A topic modeling based bibliometric exploration of hydropower research. Renew Sustain Energy Rev 2016;57:226–37.

[29] Mohammed S, Al-augby S. LSA & LDA topic modeling classification: comparison study on e-books. Indones J Electr Eng Comput Sci 2020;19:353.

[30] Maier D, Waldherr A, Miltner P, Wiedemann G, Niekler A, Keinert A, et al. Applying LDA topic modeling in communication research: Toward a valid and reliable methodology. Commun Methods Meas 2018;12:1–26.

[31] Wang H, Wang J, Zhang Y, Wang M, Mao C. Optimization of topic recognition model for news texts based on LDA. J Digit Inf Manag 2019;17:257.

[32] Babalola O, Ojokoh B, Boyinbode O. Comprehensive evaluation of LDA, NMF, and BERTopic's performance on news headline topic modeling. J Comput Theor Appl 2024;2(2):268–89.

[33] Egger R, Yu J. A topic modeling comparison between LDA, NMF, Top2Vec, and BERTopic to demystify Twitter posts. Front Sociol 2022;Volume 7 - 2022.

[34] Alangari H, Algethami N. Exploring the effects of pre-processing techniques on topic modeling of an arabic news article data set. Appl Sci 2024;14(23).

[35] He L, Han D, Zhou X, Qu Z. The voice of drug consumers: Online textual review analysis using structural topic model. Int J Env Res Public Health 2020;17(10).

[36] Lopuszynski M. Application of topic models to judgments from public procurement domain. 2014, ArXiv, abs/1412.5212.

[37] Mantyla MV, Claes M, Farooq U. Measuring LDA topic stability from clusters of replicated runs. In: Proceedings of the 12th ACM/IEEE international symposium on empirical software engineering and measurement. New York, NY, USA: Association for Computing Machinery; 2018. p. 1–4.

[38] Ding R, Nallapati R, Xiang B. Coherence-aware neural topic modeling. In: Riloff E, Chiang D, Hockenmaier J, Tsujii J, editors. Proceedings of the 2018 conference on empirical methods in natural language processing. Brussels, Belgium: Association for Computational Linguistics; 2018, p. 830–6.

[39] Glendowne P, Glendowne D. Interpretability of API call topic models: An exploratory study. In: Hawaii international conference on system sciences. 2020.

[40] Hinton GE, Roweis S. Stochastic neighbor embedding. In: Becker S, Thrun S, Obermayer K, editors. In: Adv neural inf process syst, vol. 15, MIT Press; 2002, p. 1–8.

[41] Liu H, Yang J, Ye M, James SC, Tang Z, Dong J, et al. Using t-distributed stochastic neighbor embedding (t-SNE) for cluster analysis and spatial zone delineation of groundwater geochemistry data. J Hydrol 2021;597:126146.

[42] Zhang H, Wang P, Gao X, Qi Y, Gao H. Out-of-sample data visualization using bi-kernel t-SNE. Inf Vis 2021;20(1):20–34.

[43] Rousseeuw PJ. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 1987;20:53–65.

[44] Berry M, Mohamed A, Yap B. Supervised and Unsupervised Learning for Data Science. Cham, Switzerland: Springer; 2019.

[45] Chen Z, Wu L, Cheng S, Lin P, Wu Y, Lin W. Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics. Appl Energy 2017;204:912–31.

[46] Mellit A. An embedded solution for fault detection and diagnosis of photovoltaic modules using thermographic images and deep convolutional neural networks. Eng Appl Artif Intell 2022;116:105459.

[47] Zhang L, Liu P, Zhao L, Wang G, Zhang W, Liu J. Air quality predictions with a semi-supervised bidirectional LSTM neural network. Atmos Pollut Res 2021;12(1):328–39.

[48] Momeni H, Sadoogi N, Farrokhifar M, Gharibeh HF. Fault diagnosis in photovoltaic arrays using GBSSL method and proposing a fault correction system. IEEE Trans Ind Inf 2020;16(8):5300–8.

[49] Coronato A, Naeem M, De Pietro G, Paraglioni G. Reinforcement learning for intelligent healthcare applications: A survey. Artif Intell Med 2020;109:101964.

[50] Yang Y, Tu F, Huang S, Tu Y, Liu T. Research on CNN-LSTM DC power system fault diagnosis and differential protection strategy based on reinforcement learning. Front Energy Res 2023;11.

[51] Sepúlveda-Oviedo EH, Travé-Massuyès L, Subias A, Pavlov M, Alonso C. DTW k-means clustering for fault detection in photovoltaic modules. In: XI Congreso Internacional de Ingeniería Mecánica, Mecatrónica y Automatización 2023. 2023, p. 1–2.

[52] Liu Y, Ding K, Zhang J, Li Y, Yang Z, Zheng W, et al. Fault diagnosis approach for photovoltaic array based on the stacked auto-encoder and clustering with I-V curves. Energy Convers Manag 2021;245:114603.

[53] Sepúlveda-Oviedo EH, Travé-Massuyès L, Subias A, Alonso C, Pavlov M. Hierarchical clustering and dynamic time warping for fault detection in photovoltaic systems. In: X congreso internacional ingeniería mecánica, mecatrónica y automatización. Bogotá, Colombia; 2021, p. 1–2.

[54] Reddy OY, Chatterjee S, Chakraborty AK. Layered fault detection and classification scheme for low-voltage DC microgrid with weighted KNN and decision tree. Int J Green Energy 2021;1–11.

[55] Manohar M, Koley E, Ghosh S. Enhancing the reliability of protection scheme for PV integrated microgrid by discriminating between array faults and symmetrical line faults using sparse auto encoder. IET Renew Power Gener 2019;13(9):308–17.

[56] Chen Z, Han F, Wu L, Yu J, Cheng S, Lin P, et al. Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents. Energy Convers Manag 2018;178:250–64.

[57] Garoudja E, Chouder A, Kara K, Silvestre S. An enhanced machine learning based approach for failures detection and diagnosis of PV systems. Energy Convers Manag 2017;151:496–513.

[58] Kapucu C, Cubukcu M. A supervised ensemble learning method for fault diagnosis in photovoltaic strings. Energy 2021;227:120463.

[59] Guo X, Na Z, Ma D, Lu Y, Luo X. Fault diagnosis of photovoltaic system based on machine learning model fusion. IOP Conf Ser Earth Environ Sci 2020;467(1):012073.

[60] Zhao Y, Ball R, Mosesian J, de Palma J-F, Lehman G. Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays. IEEE Trans Power Electron 2015;30(5):2848–58.

[61] Maaløe L, Winther O, Spataru S, Sera D. Condition monitoring in photovoltaic systems by semi-supervised machine learning. Energies 2020;13(3).

[62] Jaskie K, Martin J, Spanias A. PV fault detection using positive unlabeled learning. Appl Sci 2021;11(12).
[63] Murugesan DN, Anitha R, Ganesan M. N-semi regular graph-based fuzzy semi-supervised learning approach for fault detection and classification in photovoltaic arrays. IJREAT 2020;11(9):887-96.

[64] Dai S, Wang D, Li W, Zhou Q, Tian G, Dong H. Fault diagnosis of data-driven photovoltaic power generation system based on deep reinforcement learning. Math Probl Eng 2021;2021:1-10.

[65] Karaki T, Saied M, Shraim H. Deep reinforcement learning-based approach for fault-tolerant control of PV systems in smart grids. In: 2022 10th international conference on systems and control. 2022. p. 283-8.

[66] Mohamed A, Nassar A. New algorithm for fault diagnosis of photovoltaic energy systems. Int J Comput Appl 2015;114(9):26-31, Full text available.

[67] Seo G, Yoon S, Song J, Srivastava E, Hwang E. Label-free fault detection scheme for inverters of PV systems: Deep reinforcement learning-based dynamic threshold. Appl Sci 2023;13(4).

[68] Zheng H, Wang R, Xu W, Wang Y, Zhu W. Combining a HMM with a genetic algorithm for the fault diagnosis of photovoltaic inverters. J Power Electron 2017;17(4):1014-26.

[69] Zhao Y, Liu Q, Li D, Kang D, Lv Q, Shang L. Hierarchical anomaly detection and multimodal classification in large-scale photovoltaic systems. IEEE Trans Sustain Energy 2019;10(3):1351-61.

[70] Vyas S, Kumar R, Kavasseri R. Unsupervised learning in islanding studies: Applicability study for predictive detection in high solar PV penetration distribution feeders. In: 2016 IEEE uttar pradesh section international conference on electrical, computer and electronics engineering. 2016. p. 361-6.

[71] Harrou F, Taghezouit B, Khadraoui S, Dairi A, Sun Y, Hadj Arab A. Ensemble learning techniques-based monitoring charts for fault detection in photovoltaic systems. Energies 2022;15(18).

[72] Taghezouit B, Harrou F, Sun Y, Arab AH, Larbes C. A simple and effective detection strategy using double exponential scheme for photovoltaic systems monitoring. Sol Energy 2021;214:337-54.

[73] Harrou F, Saidi A, Sun Y, Khadraoui S. Monitoring of photovoltaic systems using improved kernel-based learning schemes. IEEE J Photovolt 2021;11(3):806-18.

[74] Harrou F, Dairi A, Taghezouit B, Khaldi B, Sun Y. Automatic fault detection in grid-connected photovoltaic systems via variational autoencoder-based monitoring. Energy Convers Manag 2024;314:118665.

[75] Harrou F, Taghezouit B, Sun Y. Improved kNN-based monitoring schemes for detecting faults in PV systems. IEEE J Photovolt 2019;9(3):811-21.

[76] Sepúlveda-Oviedo EH, Travé-Massuyès L, Subias A, Pavlov M, Alonso C. An ensemble learning framework for snail trail fault detection and diagnosis in photovoltaic modules. Eng Appl Artif Intell 2024;137:109068.

[77] Sepúlveda-Oviedo EH, Travé-Massuyès L, Subias A, Alonso C, Pavlov M. Feature extraction and health status prediction in PV systems. Adv Eng Inf 2022;53:101696.

[78] Hussain M, Al-Aqrabi H, Hill R. Statistical analysis and development of an ensemble-based machine learning model for photovoltaic fault detection. Energies 2022;15(15).

[79] Barbosa J, de Medeiros RLP, Ayres FAC, Filho JEC, Lucena VF, Bessa I. Fault detection for photovoltaic systems using fuzzy C-means clustering. In: 2022 IEEE 27th international conference on emerging technologies and factory automation. 2022. p. 1-5.

[80] Alrifaey M, Lim WH, Ang CK, Natarajan E, Solihin MI, Juhari MRM, et al. Hybrid deep learning model for fault detection and classification of grid-connected photovoltaic system. IEEE Access 2022;10:13852-69.

[81] Yao S, Kang Q, Zhou M, Abusorrah A, Al-Turki Y. Intelligent and data-driven fault detection of photovoltaic plants. Processes 2021;9(10).

[82] Hussain M, Dhimish M, Titarenko S, Mather P. Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters. Renew Energy 2020;155:1272-92.

[83] Lu F, Niu R, Zhang Z, Guo L, Chen J. A generative adversarial network-based fault detection approach for photovoltaic panel. Appl Sci 2022;12(4).

[84] Mayr S, Grabmair G, Reger J. Fast model-based fault detection in single-phase photovoltaic systems. In: IECON 2019 - 45th annual conference of the IEEE industrial electronics society, vol. 1, 2019, p. 4615-22.

[85] Bendary AF, Abdelaziz AY, Ismail MM, Mahmoud K, Lehtonen M, Darwish MMF. Proposed ANFIS based approach for fault tracking, detection, clearing and rearrangement for photovoltaic system. Sensors 2021;21(7).

[86] Fezai R, Mansouri M, Trabelsi M, Hajji M, Nounou H, Nounou M. Online reduced kernel GLRT technique for improved fault detection in photovoltaic systems. Energy 2019;179:1133-54.

[87] Fazai R, Abodayeh K, Mansouri M, Trabelsi M, Nounou H, Nounou M, et al. Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems. Sol Energy 2019;190:405-13.

[88] Alkatheri A, Al-Ammar E, Alotaibi M, ko W, Park S, Choi H-J. Application of artificial intelligence in PV fault detection. Sustainability 2022;14:1-27.

[89] Gao W. PV array fault detection based on deep neural network. In: 2021 IEEE green technologies conference. 2021, p. 42-7.

[90] Mandal RK, Anand N, Sahu N, Kale P. PV system fault classification using SVM accelerated by dimension reduction using PCA. In: 2020 IEEE 9th power India international conference. 2020, p. 1-6.

[91] He W, Yin D, Zhang K, Zhang X, Zheng J. Fault detection and diagnosis method of distributed photovoltaic array based on fine-tuning naive Bayesian model. Energies 2021;14(14).

[92] Dhibi K, Mansouri M, Abodayeh K, Bouzrara K, Nounou H, Nounou M. Interval-valued reduced ensemble learning based fault detection and diagnosis techniques for uncertain grid-connected PV systems. IEEE Access 2022;10:47673-86.

[93] Zgraggen J, Guo Y, Notaristefano A, Huber L. In: Physics informed deep learning for tracker fault detection in photovoltaic power plants, vol. 14, Annual Conference of the PHM Society; 2022, http://dx.doi.org/10.36001/phmconf. 2022.v14i1.3235.

[94] T. S. PV panel: irradiance, temperature, partial shading - IV curves. Mendeley Data; 2021, Available from: https://data.mendeley.com/datasets/z93gzbptf7/1, [Accessed 18 October 2023].

[95] Rashed A. In: Rashed A, editor. Fault detection dataset in photovoltaic farms. GitHub; 2023, Available from: https://github.com/amrrashed/Fault-Detection-Dataset-in-Photovoltaic-Farms, [Accessed 18 October 2023].

[96] DKASC. Desert knowledge Australia solar centre. In: DKASC, editor. DKASC; 2023, Available from: https://dkasolarcentre.com.au/locations/alice-springs, [Accessed 18 October 2023].

[97] PecanStreet. In: PecanStreet, editor. Dataport. PecanStreet; 2023, Available from: https://www.pecanstreet.org/dataport/, [Accessed 18 October 2023].

[98] Lazzaretti AE, da Costa CH, Rodrigues MP, D.Yamada G, Lexinoski G, Moritz GL, et al. In: Lazzaretti AE, editor. PV Dataset. André E. Lazzaretti; 2023, Available from: https://github.com/clayton-h-costa/pv_fault_dataset, [Accessed 18 October 2023].

[99] Bakdi A, Guichi A, Mekhilef S, Bounoua W. GPVS-faults: Experimental data for fault scenarios in grid-connected PV systems under MPPT and IPPT modes. In: Data M, editor. Mendeley Data; 2020, Available from: https://data.mendeley.com/datasets/n76t439f65/1, [Accessed 18 October 2023].

[100] Sood K, Ruppert N, Mahto R. Simulation dataset of partial shading and fault of a photovoltaic module. In: Data M, editor. Mendeley Data; 2022, Available from: https://data.mendeley.com/datasets/3fr92f4xy9/1, [Accessed 18 October 2023].

[101] Rico-Garcia M, Ceballos JPV, Paja CAR. In: Zenodo, editor. Dataset for detecting the electrical behavior of photovoltaic panels from RGB images. Zenodo; 2022, http://dx.doi.org/10.5281/zenodo.6386767.

[102] Pierdicca R, Paolanti M, Felicetti A, Piccinini F, Zingaretti P. Automatic faults detection of photovoltaic farms: solair, a deep learning-based system for thermal images. Energies 2020;13(24):6496.

[103] Alfaro-Mejía E, Loaiza-Correa H, Franco-Mejía E, Restrepo-Girón AD, Nope-Rodríguez SE. Dataset for recognition of snail trails and hot spot failures in monocrystalline si solar panels. Data Brief 2019;26:104441.

[104] Deitsch S, Christlein V, Berger S, Buerhop-Lutz C, Maier A, Gallwitz F, et al. Automatic classification of defective photovoltaic module cells in electroluminescence images. Sol Energy 2019;185:455-68. http://dx.doi.org/ 10.1016/j.solener.2019.02.067, Available from: https://github.com/zae-bayern/ elpv-dataset.

[105] Pratt L, Mattheus J, Klein R. A benchmark dataset for defect detection and classification in electroluminescence images of PV modules using semantic segmentation. Syst Soft Comput 2023;200048.

[106] Bradbury K, Saboo R, Johnson T, Malof J, Devarajan A, Zhang W, et al. Distributed solar photovoltaic array location and extent dataset for remote sensing object identification. Sci Data 2016;3:160106. http://dx.doi.org/10. 1038/sdata.2016.106.

[107] SISMET-IER. In: SISMET-IER, editor. Sistema de Información de datos Solarimétricos y Meteorológicos. SISMET-IER; 2023, Available from: https:// esolmet.ier.unam.mx/Tipos_consulta.php, [Accessed 18 October 2023].

[108] NREL. In: NREL, editor. NSRDB: National Solar Radiation Database. NREL; 2023, Available from: https://nsrdb.nrel.gov/, [Accessed 18 October 2023].

[109] Hou J, Ling Y, Yujun L. In: Zenodo, editor. Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery. Zenodo; 2021, http://dx.doi.org/10.5281/zenodo.5171712.

[110] Manso Callejo MÁ, Calimanut-lonut C. In: Zenodo, editor. SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation. Zenodo; 2023, http://dx.doi. org/10.5281/zenodo.7868082.

[111] Motahhir s. In: Data M, editor. MATLAB/Simulink model of photovoltaic cell, panel and array. Mendeley Data; 2020, Available from: https://data.mendeley. com/datasets/mmwhkt7whb/3, [Accessed 18 October 2023].

[112] s.p.a. CGRA, Pasquali P, Arnaudo E, Blanco G. In: Dataport I, editor. Piedmont photovoltaic panels dataset. IEEE Dataport; 2023, http://dx.doi.org/10.21227/ f0nc-hc38.

[113] Sepúlveda-Oviedo EH. detection and diagnosis of faults and performance losses in high-power photovoltaic power plants [Ph.D. thesis], UT3 : Université Toulouse 3 Paul Sabatier; 2023, Available from: https://laas.hal.science/tel- 04888632.
[114] Massa G, Pappalardo D, Acunzo R, Oliviero M. An overview on artificial intelligence techniques for advanced operation and maintenance of PhotoVoltaic power plants. In: 2021 5th international conference on electrical, electronics, communication, computer technologies and optimization techniques. 2021, p. 734–40.

[115] Tsai D-M, Li G-N, Li W-C, Chiu W-Y. Defect detection in multi-crystal solar cells using clustering with uniformity measures. Adv Eng Inf 2015;29(3):419–30.

[116] Mellit A, Benghanem M, Kalogirou S, Massi Pavan A. An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things. Renew Energy 2023;208:399–408.

[117] Teta A, Medkour M, Chennana A, Chouchane A, Himeur Y, Gadhafi R, et al. Enhanced fault diagnosis in grid-connected photovoltaic systems: Leveraging transfer learning and ensemble methods for superior accuracy. IEEE Access 2024;12:194786–803.

[118] Kothona D, Kouliousis A, Christoforidis GC. Fault detection and classification methodology for photovoltaic systems through AI-based digital twin. In: 2024 3rd international conference on energy transition in the mediterranean area. 2024, p. 1–5.

[119] Islam M, Rashel MR, Ahmed MT, Islam AKMK, Tlemçani M. Artificial intelligence in photovoltaic fault identification and diagnosis: A systematic review. Energies 2023;16(21).

[120] Mellit A, Kalogirou S. Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions. Renew Sustain Energy Rev 2021;143:110889.

[121] Lin C. Enhancing energy efficiency in photovoltaic systems through smart technology integration: Innovations and future perspectives. E3S Web Conf 2025;606:05004.

[122] Hu J, Lim B-H, Tian X, Wang K, Xu D, Zhang F, et al. A comprehensive review of artificial intelligence applications in the photovoltaic systems. CAAI Artif Intell Res 2024;3:9150031.

[123] Abubakar A, Jibril MM, Almeida CFM, Gemignani M, Yahya MN, Abba SI. A novel hybrid optimization approach for fault detection in photovoltaic arrays and inverters using AI and statistical learning techniques: A focus on sustainable environment. Processes 2023;11(9).

[124] Mouaad B, Samir B, Mohand D. Dynamic modeling for fault diagnosis in PV systems utilizing ai techniques based on multilayer perceptron (MLP). In: 2024 ASU international conference in emerging technologies for sustainability and intelligent systems. 2024, p. 1032–6.

[125] Lu S, Sahoo A, Ma R, Phung BT. DC series arc fault detection using machine learning in photovoltaic systems: Recent developments and challenges. In: 2020 8th international conference on condition monitoring and diagnosis. 2020, p. 416–21.

[126] Eldeghady GS, Kamal HA, Moustafa Hassan MA. Comparative analysis of the performance of supervised learning algorithms for photovoltaic system fault diagnosis. Sci Tech Energ Transit 2024;79:27.

[127] Chokr B, Chatti N, Charki A, Lemenand T, Hammoud M. Feature extraction-reduction and machine learning for fault diagnosis in PV panels. Sol Energy 2023;262:111918.

[128] Hajji M, Harkat M-F, Kouadri A, Abodayeh K, Mansouri M, Nounou H, et al. Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems. Eur J Control 2021;59:313–21.

[129] El-Gayar O, Al-Ramahi MA, Wahbeh A, Nasralah T, El Noshokaty A. A comparative analysis of the interpretability of LDA and LLM for topic modeling: The case of healthcare apps. In: Americas' conference on information systems. 2024, p. 1–12, All Faculty Scholarship. 26.

[130] Wang X, Fang A, Ounis I, Macdonald C. Evaluating similarity metrics for latent Twitter topics. In: Azzopardi L, Stein B, Fuhr N, Mayr P, Hauff C, Hiemstra D, editors. Advances in information retrieval. Cham: Springer International Publishing; 2019, p. 787–94.

[131] Blair SJ, Bi Y, Mulvenna MD. Increasing topic coherence by aggregating topic models. In: Lehner F, Fteimi N, editors. Knowledge science, engineering and management. Cham: Springer International Publishing; 2016, p. 69–81.

[132] Boyack KW, Newman D, Duhon RJ, Klavans R, Patek M, Biberstine J, et al. Clustering more than two million biomedical publications: Comparing the accuracies of nine text-based similarity approaches. PLoS One 2011;6.

[133] McInnes L, Healy J, Saul N, Großberger L. UMAP: Uniform manifold approximation and projection. J Open Source Softw 2018;3(29):861.

[134] Waagen D, Hulsey D, Godwin J, Gray D, Barton J, Farmer B. t-SNE or not t-SNE, that is the question. In: Hammoud RI, Overman TL, Mahalanobis A, editors. In: Automatic target recognition XXXI, vol. 11729, SPIE; 2021, p. 117290B.