The development of Photovoltaic (PV) technology has paved the path to the exponential growth of solar cell deployment worldwide. Nevertheless, the energy efficiency of solar cells is often limited by resulting defects that can reduce their performance and lifespan. Therefore, it is crucial to identify a set of defect detection approaches for predictive maintenance and condition monitoring of PV modules. This paper presents a comprehensive revi. The development of Photovoltaic (PV) technology has paved the path to the exponential growth of solar cell deployment worldwide. Nevertheless, the energy efficiency of solar cells is often limited by resulting defects that can reduce their performance and lifespan. Therefore, it is crucial to identify a set of defect detection approaches for predictive maintenance and condition monitoring of PV modules. This paper presents a comprehensive review of different data analysis methods for defect detection of PV systems with a high categorisation granularity in terms of types and approaches for each technique. Such approaches, introduced in the literature, were categorised into Imaging-Based Techniques (IBTs) and Electrical Testing Techniques (ETTs). Although several review papers have investigated recent solar cell defect detection techniques, they do not provide a comprehensive investigation including IBTs and ETTs with a greater granularity of the different types of each for PV defect detection systems. Types of IBTs were categorised into Infrared Thermography (IRT), Electroluminescence (EL) imaging, and Light Beam Induced Current (LBIC). On the other hand, ETTs were categorised into Current-Voltage (I-V) characteristics analysis, Earth Capacitance Measurements (ECM), Time Domain Reflectometry (TDR), Power Losses Analysis (PLA), and Voltage and Current Measurements (VCM). Approaches based on digital/signal processing and Machine Learning (ML) models for each method are included where relev. Photovoltaic systemsSolar moduleDefect detectionImaging-based techniquesElectrical testing techniquesArtificial IntelligenceFault diagnosisMachine Learning1D1-DimensionalIBTImaging-Based Technique2D2-DimensionalICAIndependent Component AnalysisADCAnalog-to-Digital ConverterIRDifferent statistical outcomes have affirmed the significance of Photovoltaic (PV) systems and grid-connected PV plants worldwide. Surprisingly, the global cumulative installed capacity of solar PV systems has massively increased since 2000 to 1,177 GW by the end of 2022. Moreover, installing PV plants has led to the exponential growth of solar cell deployment worldwide. For example, the cumulative number of solar PV installations in the UK has boomed from 29,320 in 2010 to 1,249,761 by the end of 2022. Nevertheless, the energy efficiency of solar cells is often limited by resulting defects that can reduce their performance and lifespan. Defects can disseminate power by creating new recombination pathways (losses), allowing the light to generate heat rather than electricity, or even consuming power stored in the battery bank, degrading the PV module's efficiency,. Moreover, the new generations of solar cells, such as Copper-indium-Gallium-disulfide (CIGS) and Perovskite solar cells (PSCs), come with emerging challenges related to increasing their power-conversion efficiency, reducing the fabrication cost and reducing the environmental impact when using toxic materials. For example, recent research and the manufacturing sector have shown a growing interest in the development of PSC technology due to the ease of its fabrication process and higher conversion efficiency. In fact, reports of KRICT and MIT have recently verified an efficiency of 25.2 % for PSCs,. However, one of the main ch. In this paper, data analysis methods for solar cell defect detection are categorised into two forms: 1) IBTs, which depend on analysing the deviations of optical properties, thermal patterns, or other visual features in images, and 2) ETTs, which depend on comparing the deviations of the module's measured electrical parameters from the expected electrical behaviour for detecting faults. Furthermore, both IBT and ETT adopted in the literature are investigated and categorised into a greater granularity of types of approaches as illustrated in Fig. 2. IBTs are categorised into three different types: Infrared Thermography (IRT), Electroluminescence (EL) imaging, and Light Beam Induced Current (LBIC). On the other hand, ETTs are categorised into Current-Voltage (I-V) characteristics analysis, Earth Capacitance Measurements (ECM), Time Domain Reflectometry (TDR), Power Losses Analysis (PLA), Voltage and Current Measurements (VCM), and AI-based approaches.It is also worth mentioning that the selection of these data analysis methods has been guided by a comprehensive review of the existing literature, in which an extensive analysis of the current state of research has been concluded. Based on this analysis, the presented data analysis methods were further categorised into different types and corresponding approaches.
