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Fault Diagnosis in Distribution Networks with Distributed Generation Based on Improved Graph Neural Network
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Deng-yu HU, Bao-hua WANG*, Jin-hong LIU
Science Technology and Engineering | 2025, 25(21) : 8936 - 8944
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Science Technology and Engineering | 2025, 25(21): 8936-8944
• Papers·Electrical Technology •
Fault Diagnosis in Distribution Networks with Distributed Generation Based on Improved Graph Neural Network
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Deng-yu HU, Bao-hua WANG*, Jin-hong LIU
Affiliations
  • School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Published: 2025-07-28 doi: 10.12404/j.issn.1671-1815.2405170
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The weak fault characteristics and the presence of numerous harmonic signals in distribution networks with renewable energy sources reduce the effectiveness of traditional fault diagnosis methods. A fault diagnosis method based on an improved graph neural network was proposed. Wavelet transform was applied to extract the detail coefficients of current and voltage before and after faults. Weighted projection correlation analysis was performed to calculate the correlation between electrical quantities. Highly correlated quantities were selected as inputs to construct the fault diagnosis model using a graph neural network. Fault simulation models for different voltage levels were developed in MATLAB/Simulink. The results indicate that fault signals are effectively enhanced, and faults are accurately located and classified in distribution networks with renewable energy sources at different voltage levels. Good diagnostic performance is maintained in the presence of data loss and noise, demonstrating strong robustness and generalization.

fault diagnosis in distribution networks  /  maximal overlap discrete wavelet transform  /  grey correlation degree  /  weighted gray relational projection method  /  graph neural network
Deng-yu HU, Bao-hua WANG, Jin-hong LIU. Fault Diagnosis in Distribution Networks with Distributed Generation Based on Improved Graph Neural Network[J]. Science Technology and Engineering, 2025 , 25 (21) : 8936 -8944 . DOI: 10.12404/j.issn.1671-1815.2405170
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Year 2025 volume 25 Issue 21
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Article Info
doi: 10.12404/j.issn.1671-1815.2405170
  • Receive Date：2024-07-10
  • Online Date：2026-01-13
  • Published：2025-07-28
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  • Received：2024-07-10
  • Revised：2025-04-11
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    School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
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