Traditional methods for photovoltaic panel defect detection primarily rely on manual visual inspection or basic optical detection equipment, both of which have significant limitations. Manual inspection is inefficient, prone to subjective bias, and often fails to identify subtle or hidden defects.
However, PV panels are prone to various defects such as cracks, micro-cracks, and hot spots during manufacturing, installation, and operation, which can significantly reduce power generation efficiency and shorten equipment lifespan. Therefore, fast and accurate defect detection has become a vital technical demand in the industry.
Solar PV panel automated defect detection and localisation systems typically comprise three main stages, as illustrated in Fig. 1: algorithms and software, hardware, and platforms. Each stage encompasses multiple steps. The algorithms and software stage begins with preprocessing to reduce noise and normalise sensor-captured images.
Nevertheless, review papers proposed in the literature need to provide a comprehensive review or investigation of all the existing data analysis methods for PV system defect detection, including imaging-based and electrical testing techniques with greater granularity of each category's different types of techniques.
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However, PV panels are prone to various defects such as cracks, micro-cracks, and hot spots during manufacturing, installation, and operation, which can significantly reduce power
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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
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