Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model
To address the problem of low accuracy of power transformer fault diagnosis, this study proposed a transformer fault diagnosis method based on DBSO-CatBoost model. Based on data feature extraction, this method adopted DBSO (Difference-mutation Brain Storm Optimization) algorithm to optimize CatBoost...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9648323/ |
| _version_ | 1818550786839281664 |
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| author | Mei Zhang Wanli Chen Yu Zhang Fei Liu Dongshun Yu Chaoyin Zhang Li Gao |
| author_facet | Mei Zhang Wanli Chen Yu Zhang Fei Liu Dongshun Yu Chaoyin Zhang Li Gao |
| author_sort | Mei Zhang |
| collection | DOAJ |
| description | To address the problem of low accuracy of power transformer fault diagnosis, this study proposed a transformer fault diagnosis method based on DBSO-CatBoost model. Based on data feature extraction, this method adopted DBSO (Difference-mutation Brain Storm Optimization) algorithm to optimize CatBoost model and diagnose faults. First, for data preprocessing, the ratio method was introduced to add features to the original data, the SHAP (Shapley Additive Explanations) method was applied for feature extraction, and the KPCA (Kernel Principal Component Analysis) algorithm was employed to reduce the dimension of data. Subsequently, the preprocessed data were inputted into the CatBoost model for training, and the DBSO algorithm was adopted to optimize the parameters of the CatBoost model to yield the optimal model. Lastly, the DBSO-CatBoost model was exploited to diagnose the transformer fault and output the fault type. As indicated from the example results, the accuracy of the transformer fault diagnosis based on DBSO-Catboost model could be 93.71%, 3.958% higher than that of CatBoost model and significantly exceeding that of some common models. Furthermore, compared with other preprocessing methods, the accuracy of fault diagnosis by employing the data preprocessing method proposed in this study was significantly improved. |
| first_indexed | 2024-12-12T08:51:09Z |
| format | Article |
| id | doaj.art-574e1b779cea43379482b7e73456c3ad |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T08:51:09Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-574e1b779cea43379482b7e73456c3ad2022-12-22T00:30:12ZengIEEEIEEE Access2169-35362021-01-01916876716878210.1109/ACCESS.2021.31352839648323Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost ModelMei Zhang0https://orcid.org/0000-0003-2013-7613Wanli Chen1https://orcid.org/0000-0002-4507-2177Yu Zhang2Fei Liu3Dongshun Yu4Chaoyin Zhang5Li Gao6College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, ChinaCollege of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, ChinaState Grid Xinjiang Electric Power Company Ltd., Hetian Power Supply Company Ltd., Ürümqi, Hetian, Xinjiang, ChinaCollege of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, ChinaCollege of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, ChinaCollege of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, ChinaCollege of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, ChinaTo address the problem of low accuracy of power transformer fault diagnosis, this study proposed a transformer fault diagnosis method based on DBSO-CatBoost model. Based on data feature extraction, this method adopted DBSO (Difference-mutation Brain Storm Optimization) algorithm to optimize CatBoost model and diagnose faults. First, for data preprocessing, the ratio method was introduced to add features to the original data, the SHAP (Shapley Additive Explanations) method was applied for feature extraction, and the KPCA (Kernel Principal Component Analysis) algorithm was employed to reduce the dimension of data. Subsequently, the preprocessed data were inputted into the CatBoost model for training, and the DBSO algorithm was adopted to optimize the parameters of the CatBoost model to yield the optimal model. Lastly, the DBSO-CatBoost model was exploited to diagnose the transformer fault and output the fault type. As indicated from the example results, the accuracy of the transformer fault diagnosis based on DBSO-Catboost model could be 93.71%, 3.958% higher than that of CatBoost model and significantly exceeding that of some common models. Furthermore, compared with other preprocessing methods, the accuracy of fault diagnosis by employing the data preprocessing method proposed in this study was significantly improved.https://ieeexplore.ieee.org/document/9648323/Power transformerfault diagnosiscatboost modelDBSO algorithmfeature extraction |
| spellingShingle | Mei Zhang Wanli Chen Yu Zhang Fei Liu Dongshun Yu Chaoyin Zhang Li Gao Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model IEEE Access Power transformer fault diagnosis catboost model DBSO algorithm feature extraction |
| title | Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model |
| title_full | Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model |
| title_fullStr | Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model |
| title_full_unstemmed | Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model |
| title_short | Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model |
| title_sort | fault diagnosis of oil immersed power transformer based on difference mutation brain storm optimized catboost model |
| topic | Power transformer fault diagnosis catboost model DBSO algorithm feature extraction |
| url | https://ieeexplore.ieee.org/document/9648323/ |
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