Two-Terminal Fault Location Method of Distribution Network Based on Adaptive Convolution Neural Network
When a single-phase ground fault occurs in a distribution network, it is generally allowed to operate with faults for one to two hours, which may lead to further development of the fault and even threaten the safe operation of the power system. Therefore, when a small current system has a ground fau...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9035471/ |
| _version_ | 1818735898141917184 |
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| author | Jiefeng Liang Tianjun Jing Huanna Niu Jiangbo Wang |
| author_facet | Jiefeng Liang Tianjun Jing Huanna Niu Jiangbo Wang |
| author_sort | Jiefeng Liang |
| collection | DOAJ |
| description | When a single-phase ground fault occurs in a distribution network, it is generally allowed to operate with faults for one to two hours, which may lead to further development of the fault and even threaten the safe operation of the power system. Therefore, when a small current system has a ground fault, it must be quickly diagnosed to shorten the time of operation with fault. In this paper, an adaptive convolutional neural network (ACNN)-based fault line selection method is proposed for a distribution network. This method improves the feature extraction ability of the network by improving the pooling model. Compared with deep belief network (DBN), it can improve the accuracy of fault classification by 7.86% and reduce the training time by 42.7%. On this basis, the secondary fault location is identified using the principle of two-terminal fault location. In this research, fault data obtained by Simulink simulation is used as training set, and ACNN model is built based on TensorFlow framework. The analysis of results proves that the model has a high fault recognition rate and fast convergence speed. It can be used as an auxiliary hand for fault diagnosis in distribution networks. |
| first_indexed | 2024-12-18T00:28:34Z |
| format | Article |
| id | doaj.art-cae47d3073784a6ebc56cb9eab596382 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:28:34Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cae47d3073784a6ebc56cb9eab5963822022-12-21T21:27:12ZengIEEEIEEE Access2169-35362020-01-018540355404310.1109/ACCESS.2020.29805739035471Two-Terminal Fault Location Method of Distribution Network Based on Adaptive Convolution Neural NetworkJiefeng Liang0https://orcid.org/0000-0003-2590-8923Tianjun Jing1Huanna Niu2Jiangbo Wang3College of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaWhen a single-phase ground fault occurs in a distribution network, it is generally allowed to operate with faults for one to two hours, which may lead to further development of the fault and even threaten the safe operation of the power system. Therefore, when a small current system has a ground fault, it must be quickly diagnosed to shorten the time of operation with fault. In this paper, an adaptive convolutional neural network (ACNN)-based fault line selection method is proposed for a distribution network. This method improves the feature extraction ability of the network by improving the pooling model. Compared with deep belief network (DBN), it can improve the accuracy of fault classification by 7.86% and reduce the training time by 42.7%. On this basis, the secondary fault location is identified using the principle of two-terminal fault location. In this research, fault data obtained by Simulink simulation is used as training set, and ACNN model is built based on TensorFlow framework. The analysis of results proves that the model has a high fault recognition rate and fast convergence speed. It can be used as an auxiliary hand for fault diagnosis in distribution networks.https://ieeexplore.ieee.org/document/9035471/Convolutional neural networkadaptive pooling modeltwo-terminal fault locationdistribution networksingle-phase ground fault |
| spellingShingle | Jiefeng Liang Tianjun Jing Huanna Niu Jiangbo Wang Two-Terminal Fault Location Method of Distribution Network Based on Adaptive Convolution Neural Network IEEE Access Convolutional neural network adaptive pooling model two-terminal fault location distribution network single-phase ground fault |
| title | Two-Terminal Fault Location Method of Distribution Network Based on Adaptive Convolution Neural Network |
| title_full | Two-Terminal Fault Location Method of Distribution Network Based on Adaptive Convolution Neural Network |
| title_fullStr | Two-Terminal Fault Location Method of Distribution Network Based on Adaptive Convolution Neural Network |
| title_full_unstemmed | Two-Terminal Fault Location Method of Distribution Network Based on Adaptive Convolution Neural Network |
| title_short | Two-Terminal Fault Location Method of Distribution Network Based on Adaptive Convolution Neural Network |
| title_sort | two terminal fault location method of distribution network based on adaptive convolution neural network |
| topic | Convolutional neural network adaptive pooling model two-terminal fault location distribution network single-phase ground fault |
| url | https://ieeexplore.ieee.org/document/9035471/ |
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