A Literature Review on the Use of AI and Machine Learning for Fault Identification and Classification in PV Panels

Abstract

In recent years, solar energy has drawn a lot of attention for the production of electricity. Also, owing of the advancements in technology in this area, photovoltaic (PV) systems are widely used throughout the world. To attain and maximize their effectiveness, solar PV systems do, however, require precise monitoring and routine follow-up. The PV systems are susceptible to a variety of defects, from transient to irreversible breakdowns. Determining the kind and location of faults in a PV system presents a substantial problem in order to promptly and economically maintains the system’s essential performance without interfering with its regular operation. To minimize the harm a defective PV module does and protect the PV system from further losses, a suitable fault identification system should be turned on. Various PV fault classes and fault detection methods are provided in this paper. In particular, the advantages of Artificial intelligence (AI) a Machine learning (ML) techniques for categorizing and locating various fault types are discussed. Also provided is a summary of current approaches that combine thermography techniques with various artificial intelligence tools.

Keywords: Artificial intelligence, Defect detection system, Machine learning, Photovoltaic (PV) systems.

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