Fault identification in a grid connected solar PV system using Back propagation Neural Network. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Mano Raja Paul, M.; Kannan, R.; Leeban Moses, M.; Bhuvanesh, A.
• Is Part Of: IOP conference series. Volume 1084:Issue 1(2021)
• Publication Date: 2021-03
• Subjects: Back propagation algorithm -- fault identification -- AC-GCPV system -- Matlab; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1084/1/012109
• Languages: English
• ISSNs: 1757-8981

Abstract

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.