# Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems
## 1 Introduction
### Methods for Fault Detection and Classification  
Detecting anomalies in PV modules holds paramount importance due to their capacity to induce significant power losses and performance degradation. Predicting equipment faults and failures helps reduce maintenance costs, avoid total failures, and prevent unwanted repair and replacement expenses. Such predictive analytics also safeguard against financial losses, indirect impacts on customer confidence, and the deterioration of a company's image, ensuring long-term profitability and customer retention. Broadly, faults within PV systems can be categorized into two primary types: irreparable faults stemming from mechanical or electrical issues like open circuits, short circuits, and PV cell deterioration, and momentary power loss faults attributed to factors like shading, such as cloud-induced shadows [19]. Common permanent defects encountered in PV systems include mismatches, arising from the integration of cells with dissimilar physical traits or operating conditions; cracks, prevalent and varying in formations, directions, and sizes; discolorations caused by internal factors like lower mechanical properties, high temperatures, and humidity; soiling, which pertains to the accumulation of dirt on the surface; delamination of bonds over time; and snail tracks or trails--black or brown lines of discodoration on cell surfaces--emerging after months or years of panel operation [20]. According to the study of [PERSON] et al., three categories of PV system faults exist: (i) module-related faults, (ii) string- and system-related faults, and (iii) racking and system balance issues [21]. In the case of module faults, they encompass concerns such as cell hotspots, diode malfunctions, fractured or soiled modules, coating, and fogging complications, as well as junction box overheating. For the string and systems faults, there are wiring issues (reversed polarity, frayed cables), charge controller issues, and inverter and fuse failures. On the other hand, [PERSON] et al. [22] stated that typical faults in a PV system include irradiance change, ground faults, line-line faults, arc faults, and so on. No matter how the faults are classified by researchers and field experts, the important thing is the rapid detection using the monitoring systems and diagnosis and solution of the fault. Among the diverse PV faults, this study focuses on the hotspot phenomenon, recognized as the principal module defect, and bypass diode failure. Therefore, both have garnered considerable attention as a prominent research subject within the photovoltaic systems domain. Hotspots are created mainly because cracked PV cells with broken contacts conduct electric current only on part of their surface area, hence they can be mentioned as localized heat sources that can be present in part of the PV module, leading to locally increased temperature in the solar cells. [PERSON] [23; 24] highlighted the significance of hotspotting, where a cell or group of cells heats up significantly compared to adjacent solar cells, decreasing the optimum power generated. A solitary fault within a cell holds the potential to propagate neighboring modules, culminating in a complete operational breakdown [25; 26]. On the other hand, the purpose of using diodes in photovoltaic panels is to provide unidirectional current flow. Diode failures are mostly caused by incorrect connection and overheating [27]. [PERSON] et al. [28] emphasized that defective bypass diodes are often the largest factor leading to power loss in solar modules and reported on failure mechanisms by investigating shunted bypass diodes from a rooftop installation. Over the past decade, various methods have been developed for detecting and diagnosing the above-mentioned faults in PV systems. [PERSON] et al. made an introduction to different methods including a model-based approach, procedures using current and voltage indicators, a method by setting a power residual threshold in their article [29].