An Auxiliary Fault Identification Strategy of Flexible HVDC Grid Based on Convolutional Neural Network With Branch Structures
Lightning disturbance may be misjudged as dc fault by the primary protection in the flexible high voltage dc (HVDC) grid. To solve this problem, an auxiliary fault identification strategy based on convolutional neural network with branch structures (BR-CNN) is proposed in this paper. In the proposed...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9123377/ |
| _version_ | 1818664328096645120 |
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| author | Jun Mei Rui Ge Zhong Liu Xin Zhan Guangyao Fan Pengfei Zhu Wu Chen |
| author_facet | Jun Mei Rui Ge Zhong Liu Xin Zhan Guangyao Fan Pengfei Zhu Wu Chen |
| author_sort | Jun Mei |
| collection | DOAJ |
| description | Lightning disturbance may be misjudged as dc fault by the primary protection in the flexible high voltage dc (HVDC) grid. To solve this problem, an auxiliary fault identification strategy based on convolutional neural network with branch structures (BR-CNN) is proposed in this paper. In the proposed scheme, the voltage and current characteristic matrix is constructed as the input matrix of BR-CNN model and the output categories include positive pole-to-ground (PTG) fault and lightning disturbance. Voltage and current branches are constructed to extract high-level local features of input data layer by layer, and main branch is designed to realize the comprehensive utilization of voltage and current information. Through autonomous learning of the model, the nonlinear mapping relationship between input and output is constructed. The method only uses the single-terminal quantities, and can be used as an auxiliary criterion to improve the reliability of the primary protection. The test results verify the effectiveness of the method, and the recognition accuracy is better than the traditional classification models. |
| first_indexed | 2024-12-17T05:30:59Z |
| format | Article |
| id | doaj.art-261e33eb70b844a8895af1a586996b8b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:30:59Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-261e33eb70b844a8895af1a586996b8b2022-12-21T22:01:43ZengIEEEIEEE Access2169-35362020-01-01811592211593110.1109/ACCESS.2020.30044349123377An Auxiliary Fault Identification Strategy of Flexible HVDC Grid Based on Convolutional Neural Network With Branch StructuresJun Mei0https://orcid.org/0000-0003-1543-6328Rui Ge1https://orcid.org/0000-0001-7504-6757Zhong Liu2Xin Zhan3Guangyao Fan4Pengfei Zhu5Wu Chen6https://orcid.org/0000-0002-1835-7564School of Electrical Engineering, Southeast University, Nanjing, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, ChinaState Grid Yangzhou Power Supply Company, Yangzhou, ChinaState Grid Yangzhou Power Supply Company, Yangzhou, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, ChinaLightning disturbance may be misjudged as dc fault by the primary protection in the flexible high voltage dc (HVDC) grid. To solve this problem, an auxiliary fault identification strategy based on convolutional neural network with branch structures (BR-CNN) is proposed in this paper. In the proposed scheme, the voltage and current characteristic matrix is constructed as the input matrix of BR-CNN model and the output categories include positive pole-to-ground (PTG) fault and lightning disturbance. Voltage and current branches are constructed to extract high-level local features of input data layer by layer, and main branch is designed to realize the comprehensive utilization of voltage and current information. Through autonomous learning of the model, the nonlinear mapping relationship between input and output is constructed. The method only uses the single-terminal quantities, and can be used as an auxiliary criterion to improve the reliability of the primary protection. The test results verify the effectiveness of the method, and the recognition accuracy is better than the traditional classification models.https://ieeexplore.ieee.org/document/9123377/Convolutional neural network (CNN)dc faultfault identificationlightning disturbanceHVDC |
| spellingShingle | Jun Mei Rui Ge Zhong Liu Xin Zhan Guangyao Fan Pengfei Zhu Wu Chen An Auxiliary Fault Identification Strategy of Flexible HVDC Grid Based on Convolutional Neural Network With Branch Structures IEEE Access Convolutional neural network (CNN) dc fault fault identification lightning disturbance HVDC |
| title | An Auxiliary Fault Identification Strategy of Flexible HVDC Grid Based on Convolutional Neural Network With Branch Structures |
| title_full | An Auxiliary Fault Identification Strategy of Flexible HVDC Grid Based on Convolutional Neural Network With Branch Structures |
| title_fullStr | An Auxiliary Fault Identification Strategy of Flexible HVDC Grid Based on Convolutional Neural Network With Branch Structures |
| title_full_unstemmed | An Auxiliary Fault Identification Strategy of Flexible HVDC Grid Based on Convolutional Neural Network With Branch Structures |
| title_short | An Auxiliary Fault Identification Strategy of Flexible HVDC Grid Based on Convolutional Neural Network With Branch Structures |
| title_sort | auxiliary fault identification strategy of flexible hvdc grid based on convolutional neural network with branch structures |
| topic | Convolutional neural network (CNN) dc fault fault identification lightning disturbance HVDC |
| url | https://ieeexplore.ieee.org/document/9123377/ |
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