Fault identification in a grid connected solar PV system using Back propagation Neural Network

Abstract

Albeit the government buoy up the penetration of renewable energy sources (RES) particularly solar photovoltaic (PV) system, the dependency on fossil fuels is still growing. The power generation using solar PV system may enhance when the enactment of solar PV system is improved. The faults occurred in the system is an important performance degradation factor. Incessant studies have been performed to identify and mitigate the faults. Currently, several smart techniques are utilized to identify the faults rapidly. In this study, Back Propagation Neural Network (BPNN) has been implemented to identify the faults. The output power get degraded when the faults happened in source side, Maximum Power Point Tracking (MPPT), DC-DC converter, rectifier and grid. The investigations has performed on 100 kW solar PV system using Matlab. The outcomes imply that the proposed method has detected the faults quickly, economically and effectively.

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

“…Deep learning models can more accurately detect and identify overlays on PV panels by learning patterns and features in large datasets [30]. Compared with traditional methods, deep learning-based technology enables faster and more reliable overlay detection, improves the efficiency of PV panel maintenance, and ensures the optimal performance and power generation efficiency of PV systems [31].…”