Potential measurement techniques for photovoltaic module failure diagnosis: A review

Abstract Various characterization methods are used for the detection of PV (photovoltaic) module defects. However, these methods yield different results with varying uncertainties, depending on the measuring apparatus, data acquisition system, and filtering standards. This imposes the risk of accurately estimating the location and influence of defects of a PV module. The immediate identification and quantification of degraded solar panels have the direct cost-benefit of preventing PV module failure. The replacement of a PV system could cost far more than the cost of the module itself. Therefore, the challenges involved with solar panel defect detection techniques are discussed along with a summary of the conventional and emerging characterization technologies that enable accurate identification of the degradation source and extension of PV modules' useful lifetime. One hundred and twenty-six studies are reviewed, of which 60% deal with indoor, 40% outdoor, and 7% cover both indoor and outdoor defect detection techniques. Cell-cracks (23%) and hotspots (18%) are the most reported sources of PV module defects. The reviewed publications provide strong support for the claims that the I–V curve measurement is more handy, cost-effective, and provides instant feedback to verify the PV module condition. Simultaneously, the clustering-based computation method is relatively new and imposes several challenges, such as providing reliable predefined data with clusters optimization. Therefore, in this paper, the primary factors that degrade PV systems are investigated. The current best techniques for characterizing defects in PV systems are then overviewed, and their advantages and limitations are discussed.