Failures of Photovoltaic modules and their Detection: A Review

M. Waqar Akram, Guiqiang Li, Yi Jin and Xiao Chen

Applied Energy, 2022, vol. 313, issue C, No S0306261922002677

Abstract: Photovoltaic (PV) has emerged as a promising and phenomenal renewable energy technology in the recent past and the PV market has developed at an exponential rate during the time. However, a large number of early failure and degradation cases are also observed in the field. Besides these, there are fire risks associated with PV modules installed in the field, roof-mounted and building integrated PV systems, as modules contain combustible materials. The fire is caused by different failures and faults such as electrical arcs, short circuits, and hotspots. The timely, fast and accurate detection and measurement of failures is important to produce efficient and durable modules. Conventional visual monitoring and assessment process is commonly used in the field, which is mainly dependent upon human abilities and often involve human error. Moreover, it is only practicable on small-scale and requires long time. With the rising use of PV solar energy and ongoing installation of large-scale PV power plants worldwide, the automation of PV monitoring and assessment methods becomes important.

Keywords: Photovoltaic cells; Module failures and fire risks; Defect detection; Artificial intelligence and deep learning; Electroluminescence and Infrared imaging (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (16)

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DOI: 10.1016/j.apenergy.2022.118822

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