GIS partial discharge fault diagnosis method based on SGMD-LSTM
To accurately diagnose partial discharge faults in Gas Insulated Switchgear (GIS), a fault diagnosis method based on Symplectic Geometric Mode Decomposition (SGMD) and improved Long Short Term Memory (LSTM) is proposed. SGMD is introduced to decompose partial discharge signals. Multidimensional feat...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
National Computer System Engineering Research Institute of China
2025-02-01
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| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000170265 |
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| Summary: | To accurately diagnose partial discharge faults in Gas Insulated Switchgear (GIS), a fault diagnosis method based on Symplectic Geometric Mode Decomposition (SGMD) and improved Long Short Term Memory (LSTM) is proposed. SGMD is introduced to decompose partial discharge signals. Multidimensional features are extracted from signals and a mixed time-frequency-entropy feature vector is constructed. The Osprey-Cauchy-Sparrow Search Algorithm (OCSSA) is used to adaptively optimize the number of hidden layer nodes and learning rate of LSTM. Finally, OCSSA-LSTM is used for partial discharge identification. The experimental results show that OCSSA has significant improvements in convergence accuracy and speed, and performs excellently. Compared with other fault diagnosis models, the accuracy of the OCSSA-LSTM fault diagnosis model can reach up to 97.5%, and it can also accurately identify actual GIS operation and maintenance data. |
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| ISSN: | 0258-7998 |