Multi-Dimensional Feature Perception Network for Open-Switch Fault Diagnosis in Grid-Connected PV Inverters

Yuxuan Xie, Yaoxi He, Yong Zhan, Qianlin Chang, Keting Hu () and Haoyu Wang
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Yuxuan Xie: China Yangtze Power Co., Ltd., Wuhan 430014, China
Yaoxi He: China Yangtze Power Co., Ltd., Wuhan 430014, China
Yong Zhan: China Yangtze Power Co., Ltd., Wuhan 430014, China
Qianlin Chang: China Yangtze Power Co., Ltd., Wuhan 430014, China
Keting Hu: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610032, China
Haoyu Wang: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610032, China

Energies, 2025, vol. 18, issue 15, 1-16

Abstract: Intelligent monitoring and fault diagnosis of PV grid-connected inverters are crucial for the operation and maintenance of PV power plants. However, due to the significant influence of weather conditions on the operating status of PV inverters, the accuracy of traditional fault diagnosis methods faces challenges. To address the issue of open-circuit faults in power switching devices, this paper proposes a multi-dimensional feature perception network. This network captures multi-scale fault features under complex operating conditions through a multi-dimensional dilated convolution feature enhancement module and extracts non-causal relationships under different conditions using convolutional feature fusion with a Transformer. Experimental results show that the proposed network achieves fault diagnosis accuracies of 97.3% and 96.55% on the inverter dataset and the generalization performance dataset, respectively.

Keywords: PV grid-connected inverter; intelligent fault diagnosis; open-circuit fault (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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