Publication

Abstract : This chapter presents the process of detecting the faults and diagnosis of solar photovoltaic (SPV) system/plant based on machine learning algorithms. In the case of manual operation, the steps to be followed are visual examination of individual parts and the entire plant, insulation resistance analysis and I-V curve tracing analysis of the SPV system, infrared (IR) thermography of SPV modules, failure mode and effect analysis (FMEA) approach, application of machine learning and prediction, and sensor analysis in real-time scenarios. The four main stages of fault identification and diagnosing of SPV system/plant using machine learning algorithm are discussed elaborately. This chapter also addresses anomaly detection and predictive maintenance, which simplifies the maintenance operators’ decision-making process.