Power Transformer Fault Diagnosis Based on Improved BP Neural Network
Power transformers are complex and extremely important piece of electrical equipment in a power system, playing an important role in changing voltage and transmitting electricity. Its operational status directly affects the stability and safety of power grids, and once a fault occurs, it may lead to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/16/3526 |
| _version_ | 1797584939976228864 |
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| author | Yongshuang Jin Hang Wu Jianfeng Zheng Ji Zhang Zhi Liu |
| author_facet | Yongshuang Jin Hang Wu Jianfeng Zheng Ji Zhang Zhi Liu |
| author_sort | Yongshuang Jin |
| collection | DOAJ |
| description | Power transformers are complex and extremely important piece of electrical equipment in a power system, playing an important role in changing voltage and transmitting electricity. Its operational status directly affects the stability and safety of power grids, and once a fault occurs, it may lead to significant economic losses and social impacts. The traditional detection methods rely on the technical level of power system operation and maintenance personnel, and are based on Dissolved Gas Analysis (DGA) technology, which analyzes the components of dissolved gases in transformer oil for preliminary fault diagnosis. However, with the increasing accuracy and intelligence requirements for transformer fault diagnosis in power grids, the DGA analysis method is no longer able to meet the requirements. Therefore, this article proposes an improved transformer fault diagnosis method based on a residual BP neural network. This method deepens the BP neural network by stacking multiple residual network modules, and fuses and expands gas feature information through an improved BP neural network. In the improved residual BP neural network, SVM is introduced to judge the extracted feature vectors at each layer, screen out feature vectors with high accuracy, and increase their weights. The feature vector with the highest cumulative weight is selected as an input for transformer fault diagnosis. This method utilizes multi-layer neural network mapping to extract gas feature information with more significant feature differences after fusion expansion, thereby effectively improving diagnostic accuracy. The experimental results show that, compared with traditional BP neural network methods, the proposed algorithm has higher accuracy in transformer fault diagnosis, with an accuracy rate of 92%, which can ensure the sustainable, normal, and safe operation of power grids. |
| first_indexed | 2024-03-10T23:59:46Z |
| format | Article |
| id | doaj.art-f2d4f5878ead4da689a50518992569ed |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T23:59:46Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-f2d4f5878ead4da689a50518992569ed2023-11-19T00:54:56ZengMDPI AGElectronics2079-92922023-08-011216352610.3390/electronics12163526Power Transformer Fault Diagnosis Based on Improved BP Neural NetworkYongshuang Jin0Hang Wu1Jianfeng Zheng2Ji Zhang3Zhi Liu4School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, ChinaSchool of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, ChinaSchool of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, ChinaSchool of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, ChinaJiangsu Yude Xingyan Intelligent Technology Co., Ltd., Changzhou 213164, ChinaPower transformers are complex and extremely important piece of electrical equipment in a power system, playing an important role in changing voltage and transmitting electricity. Its operational status directly affects the stability and safety of power grids, and once a fault occurs, it may lead to significant economic losses and social impacts. The traditional detection methods rely on the technical level of power system operation and maintenance personnel, and are based on Dissolved Gas Analysis (DGA) technology, which analyzes the components of dissolved gases in transformer oil for preliminary fault diagnosis. However, with the increasing accuracy and intelligence requirements for transformer fault diagnosis in power grids, the DGA analysis method is no longer able to meet the requirements. Therefore, this article proposes an improved transformer fault diagnosis method based on a residual BP neural network. This method deepens the BP neural network by stacking multiple residual network modules, and fuses and expands gas feature information through an improved BP neural network. In the improved residual BP neural network, SVM is introduced to judge the extracted feature vectors at each layer, screen out feature vectors with high accuracy, and increase their weights. The feature vector with the highest cumulative weight is selected as an input for transformer fault diagnosis. This method utilizes multi-layer neural network mapping to extract gas feature information with more significant feature differences after fusion expansion, thereby effectively improving diagnostic accuracy. The experimental results show that, compared with traditional BP neural network methods, the proposed algorithm has higher accuracy in transformer fault diagnosis, with an accuracy rate of 92%, which can ensure the sustainable, normal, and safe operation of power grids.https://www.mdpi.com/2079-9292/12/16/3526transformerfault diagnosisimproved BP neural network |
| spellingShingle | Yongshuang Jin Hang Wu Jianfeng Zheng Ji Zhang Zhi Liu Power Transformer Fault Diagnosis Based on Improved BP Neural Network Electronics transformer fault diagnosis improved BP neural network |
| title | Power Transformer Fault Diagnosis Based on Improved BP Neural Network |
| title_full | Power Transformer Fault Diagnosis Based on Improved BP Neural Network |
| title_fullStr | Power Transformer Fault Diagnosis Based on Improved BP Neural Network |
| title_full_unstemmed | Power Transformer Fault Diagnosis Based on Improved BP Neural Network |
| title_short | Power Transformer Fault Diagnosis Based on Improved BP Neural Network |
| title_sort | power transformer fault diagnosis based on improved bp neural network |
| topic | transformer fault diagnosis improved BP neural network |
| url | https://www.mdpi.com/2079-9292/12/16/3526 |
| work_keys_str_mv | AT yongshuangjin powertransformerfaultdiagnosisbasedonimprovedbpneuralnetwork AT hangwu powertransformerfaultdiagnosisbasedonimprovedbpneuralnetwork AT jianfengzheng powertransformerfaultdiagnosisbasedonimprovedbpneuralnetwork AT jizhang powertransformerfaultdiagnosisbasedonimprovedbpneuralnetwork AT zhiliu powertransformerfaultdiagnosisbasedonimprovedbpneuralnetwork |