Solar Photovoltaic System Performance Improvement Using a New Fault Identification Technique

Abstract

Identifying faults in the photovoltaic (PV) arrays is very much essential in improving the PV system’s safety and reliability. Solar PV arrays operate with non-linear characteristics, installed with maximum power point trackers (MPPT’s), and blocking diodes cause mismatch levels. Line-to-line and line-to-ground faults are identified, and the faulted circuits are isolated by means of over current protection devices (OCPD) and ground fault protection devices (GFPD). In order to improve the accuracy of fault detection, artificial intelligence (AI)-based techniques like Fuzzy inference, wavelet, support vector machine, and k-nearest neighbors are used. The drawback of AI-based techniques are (1) requirement of large dataset for effective fault identification and also show incompatibility if there is low irradiation and (2) require a larger number of voltage and current sensors. An experimental setup of 160 W, 4 × 4 solar PV array having PV modules (SPB) is subjected to different fault conditions (CS), and the faults are identified using the minimum number of sensors. The faults that are not detected by the conventional methods are detected using this proposed method, and the power gain due to the fault identification is around 152% which is 97 W in the PV array.