Research on Characteristic Analysis and Identification Methods for DC-Side Grounding Faults in Grid-Connected Photovoltaic Inverters

Wanli Feng (), Lei Su, Cao Kan, Mingjiang Wei and Changlong Li
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Wanli Feng: State Grid Hubei Electric Power Research Institute, Wuhan 430074, China
Lei Su: State Grid Hubei Electric Power Research Institute, Wuhan 430074, China
Cao Kan: State Grid Hubei Electric Power Research Institute, Wuhan 430074, China
Mingjiang Wei: State Grid Hubei Electric Power Research Institute, Wuhan 430074, China
Changlong Li: School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410014, China

Energies, 2025, vol. 18, issue 13, 1-27

Abstract: The analysis and accurate identification of DC-side grounding faults in grid-connected photovoltaic (PV) inverters is a critical step in enhancing operation and maintenance capabilities and ensuring the safe operation of PV grid-connected systems. However, the characteristics of DC-side grounding faults remain unclear, and effective methods for identifying such faults are lacking. To address the need for leakage characteristic analysis and fault identification of DC-side grounding faults in grid-connected PV inverters, this paper first establishes an equivalent analysis model for DC-side grounding faults in three-phase grid-connected inverters. The formation mechanism and frequency-domain characteristics of residual current under DC-side fault conditions are analyzed, and the specific causes of different frequency components in the residual current are identified. Based on the leakage current mechanisms and statistical characteristics of grid-connected PV inverters, a multi-type DC-side grounding fault identification method is proposed using the light gradient-boosting machine (LGBM) algorithm. In the simulation case study, the proposed fault identification method, which combines mechanism characteristics and statistical characteristics, achieved an accuracy rate of 99%, which was significantly superior to traditional methods based solely on statistical characteristics and other machine learning algorithms. Real-time simulation verification shows that introducing mechanism-based features into grid-connected photovoltaic inverters can significantly improve the accuracy of identifying grounding faults on the DC side.

Keywords: photovoltaic grid-connected inverter; DC-side grounding fault; mechanistic characteristics; statistical features; fault diagnosis; LGBM algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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