Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods
Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems. However, traditional intelligent methods limit the use of the physical structures and data information of power networks. To this end, this study proposes a fault...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9998464/ |
| _version_ | 1797942165118124032 |
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| author | Jiaxiang Hu Weihao Hu Jianjun Chen Di Cao Zhengyuan Zhang Zhou Liu Zhe Chen Frede Blaabjerg |
| author_facet | Jiaxiang Hu Weihao Hu Jianjun Chen Di Cao Zhengyuan Zhang Zhou Liu Zhe Chen Frede Blaabjerg |
| author_sort | Jiaxiang Hu |
| collection | DOAJ |
| description | Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems. However, traditional intelligent methods limit the use of the physical structures and data information of power networks. To this end, this study proposes a fault diagnostic model for distribution systems based on deep graph learning. This model considers the physical structure of the power network as a significant constraint during model training, which endows the model with stronger information perception to resist abnormal data input and unknown application conditions. In addition, a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability. This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults. In addition, a multi-task learning framework is constructed for fault location and fault type analysis, which improves the performance and generalization ability of the model. The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model. Finally, different fault conditions, topological changes, and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model. Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods. |
| first_indexed | 2024-04-10T20:04:07Z |
| format | Article |
| id | doaj.art-d8eee3065eec4c0485bf5f16235d3d88 |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-04-10T20:04:07Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-d8eee3065eec4c0485bf5f16235d3d882023-01-27T00:00:52ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202023-01-01111355110.35833/MPCE.2022.0002049998464Fault Location and Classification for Distribution Systems Based on Deep Graph Learning MethodsJiaxiang Hu0Weihao Hu1Jianjun Chen2Di Cao3Zhengyuan Zhang4Zhou Liu5Zhe Chen6Frede Blaabjerg7Sschool of Mechanical and Electrical engineering, University of Electronic Science and Technology of China,Chengdu,ChinaSschool of Mechanical and Electrical engineering, University of Electronic Science and Technology of China,Chengdu,ChinaSschool of Mechanical and Electrical engineering, University of Electronic Science and Technology of China,Chengdu,ChinaSschool of Mechanical and Electrical engineering, University of Electronic Science and Technology of China,Chengdu,ChinaSschool of Mechanical and Electrical engineering, University of Electronic Science and Technology of China,Chengdu,ChinaSiemens Gamesa Renewable Energy A/S,Lyngby,DenmarkAalborg University,Aalborg,DenmarkAalborg University,Aalborg,DenmarkAccurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems. However, traditional intelligent methods limit the use of the physical structures and data information of power networks. To this end, this study proposes a fault diagnostic model for distribution systems based on deep graph learning. This model considers the physical structure of the power network as a significant constraint during model training, which endows the model with stronger information perception to resist abnormal data input and unknown application conditions. In addition, a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability. This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults. In addition, a multi-task learning framework is constructed for fault location and fault type analysis, which improves the performance and generalization ability of the model. The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model. Finally, different fault conditions, topological changes, and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model. Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods.https://ieeexplore.ieee.org/document/9998464/Fault diagnosisfault locationfault type analysisdistribution systemdeep graph learningmulti-task learning |
| spellingShingle | Jiaxiang Hu Weihao Hu Jianjun Chen Di Cao Zhengyuan Zhang Zhou Liu Zhe Chen Frede Blaabjerg Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods Journal of Modern Power Systems and Clean Energy Fault diagnosis fault location fault type analysis distribution system deep graph learning multi-task learning |
| title | Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods |
| title_full | Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods |
| title_fullStr | Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods |
| title_full_unstemmed | Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods |
| title_short | Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods |
| title_sort | fault location and classification for distribution systems based on deep graph learning methods |
| topic | Fault diagnosis fault location fault type analysis distribution system deep graph learning multi-task learning |
| url | https://ieeexplore.ieee.org/document/9998464/ |
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