A novel method for fault diagnosis in photovoltaic arrays used in distribution power systems

Abstract This study addresses the critical issue of fault diagnosis in photovoltaic (PV) arrays, considering the increasing integration of distributed PV systems into power grids. The research employs a novel approach that combines artificial neural networks, specifically radial basis functions (RBFs), with machine learning techniques. The methodology involves training the RBF neural network using input features like voltage, current, temperature, and irradiance, derived from the PV array, to detect and classify various fault types. Notably, it comprehensively evaluates the accuracy of this approach, with a particular focus on detecting maximum power point tracking (MPPT) and mismatch faults. The findings reveal significant advantages, in which the proposed method outperforms existing techniques, achieving an approximately 20% increase in accuracy, with fault detection rates for specific faults ranging from 81.29 to 93.44%. Simulation results represent that by leveraging RBFs within neural networks, it offers improved fault detection and classification, making it a valuable advancement in the field of PV fault diagnosis.