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The escalating prevalence of renewable energy, particularly solar energy, is on a rapid incline, not solely within developed nations, but also evident in oil-producing countries. Various challenges confront photovoltaic systems, with fault detection emerging as a pivotal issue. Artificial intelligence (AI) stands out as a prominent contemporary technique for error identification owing to its adeptness in extracting signal and image attributes. Within this investigation, a deep learning methodology grounded in artificial neural networks and Internet of things technology was harnessed for the purpose of fault detection and characterization in a photovoltaic system. The fault detection algorithm that has been implemented relies predominantly on simulating the PV system and utilizing real-time data collected from sensors. Various metrics are scrutinized and juxtaposed between the PV system simulation and real-time data. The utilization of deep learning technique involves leveraging data acquired from the Internet of Things (IoT) for fault detection and localization purposes. The outcomes of the simulation demonstrated the efficacy of employing deep learning in successful fault detection and classification.
Al-Obaidi et al. (Mon,) studied this question.