IoT-based monitoring and shading faults detection for a PV water pumping system using deep learning approach

Abstract

One of the major challenges facing photovoltaic (PV) systems is fault detection. Artificial intelligence (AI) is one of the main popular techniques used in error detection due to its ability to extract signal and image features. In this paper, a deep learning approach based on convolutional neural network (CNN) and internet of things (IoT) technology are used to detect and locate shading faults for a PV water pumping system. The current and voltage signals generated by the PV panels as well as temperature and radiation were used to convert them into 3D images and then upload to a deep learning algorithm. The PV system and fault detection algorithms were simulated by MATLAB. The obtained results indicate that the performance of the proposed deep learning approach to detect and locate faults is better than the traditional statistical methods and other machine learning methods.