A review of automated solar photovoltaic defect detection systems: Approaches, challenges, and future orientations

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 re-view 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 re-view 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 relevant. Moreover, the paper critically analyses the advantages and disadvantages of each of the adopted techniques, which can be referred to by future studies to identify the most suitable method considering the use-case's requirements and setting. The adoption of each of the reviewed techniques depends on several factors, including the deployment scale, the targeted defects for detection, and the required location of defect analysis in the PV system, which are expanded further in the presented analysis. From a high-level perspective, while IBTs provide a high-resolution visual representation of the module surface, allowing for the detection and diagnosis of small structural defects that may be missed by other techniques, ETTs can detect electrical faults beyond the PV module's surface. On the IBT level, the most notable adopted techniques in the literature are IRT-and EL-based. While IRT techniques are more practical for large-scale applications than EL imaging, the latter is considered a non-intrusive technique that is highly efficient in localising defects of solar cells. The paper also discusses challenges observed in the state-of-the-art related to data availability, real-time monitoring, accurate measurements, computational efficiency, and dataset distribution, and reviews data pre-processing and augmentation approaches that can address some of these challenges. Furthermore, potential future orientations are identified, addressing the limitations of PV defect detection systems.

Automated summary

This paper provides a comprehensive review of different data analysis methods for defect detection in PV systems. The methods are categorized into imaging-based techniques (IBTs) and electrical testing techniques (ETTs). IBTs include infrared thermography, electroluminescence imaging, and light beam induced current, while ETTs include current-voltage characteristics analysis, earth capacitance measurements, time domain reflectometry, power losses analysis, and voltage and current measurements. The paper also critically analyzes the advantages and disadvantages of each method and discusses challenges related to data availability, real-time monitoring, accurate measurements, computational efficiency, and dataset distribution. The paper concludes with potential future directions for PV defect detection systems.