A Method Based on NGO-HKELM for the Autonomous Diagnosis of Semiconductor Power Switch Open-Circuit Faults in Three-Phase Grid-Connected Photovoltaic Inverters

• Published in: Sustainability
• Main Authors: Xiao Xu, Teng Zhang, Ziwen Qiu, Hui Gao, Sanli Zhu
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: photovoltaic inverters; fault detection; fault localization; maximum relevance-minimum redundancy; hybrid kernel extreme learning machine; northern goshawk optimization; Environmental effects of industries and plants; TD194-195; Renewable energy sources; TJ807-830; Environmental sciences; GE1-350; Northern Goshawk
• Online Access: https://doi.org/10.3390/su15129588; https://doaj.org/article/f776eab4a6554cb3a676dc6524d68d5b

With accelerating grid decarbonization and technological breakthroughs, grid-connected photovoltaic (PV) systems are continuously connected to distribution networks at all voltage levels. As the grid interaction interfaces between PV panels and the distribution network, PV inverters must operate flawlessly to avoid energy and financial losses. As the failure of semiconductor switches is the leading cause of abnormal operation of PV inverters and typically cannot be detected by internal protection circuits, this paper aims to develop a method for the autonomous diagnosis of semiconductor power switch open-circuit faults in three-phase grid-connected PV inverters. In this study, a ReliefF-mRMR-based multi-domain feature selection method is designed to ensure the completeness of the fault characteristics. An NGO-HKELM-based classification method is proposed to guarantee the desired balance between generalization and exploration capability. The proposed method overcomes the common problems of poor training efficiency and imbalances between generalization and exploration capabilities. The performance of the proposed method is verified with the detection of switch OC faults in a three-phase H-bridge inverter and neutral-point-clamped inverter, with diagnostic accuracy of 100% and 99.46% respectively.