Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method
Power transformers, as essential equipment for electricity transmission, may fail due to insulation degradation. Predicting the failure rate of power transformers precisely is beneficial to decision-making. Currently, uncertainties of the prediction have not been deeply discussed. Besides, predictio...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
China electric power research institute
2025-01-01
|
| Series: | CSEE Journal of Power and Energy Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10026211/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849770742934142976 |
|---|---|
| author | Weixin Zhang Changzheng Shao Wei Huang Bo Hu Jiahao Yan Kaigui Xie Maosen Cao Zhengze Wei |
| author_facet | Weixin Zhang Changzheng Shao Wei Huang Bo Hu Jiahao Yan Kaigui Xie Maosen Cao Zhengze Wei |
| author_sort | Weixin Zhang |
| collection | DOAJ |
| description | Power transformers, as essential equipment for electricity transmission, may fail due to insulation degradation. Predicting the failure rate of power transformers precisely is beneficial to decision-making. Currently, uncertainties of the prediction have not been deeply discussed. Besides, prediction accuracy is not high enough. This paper proposes a decomposition-based Bayesian deep learning (BDL) method to predict the failure rate of power transformers. Both the model uncertainty related to distribution of the model's weights and the inherent uncertainty associated with random noise can be captured by BDL. Uncertainties of prediction results are depicted with confidence intervals. Moreover, prediction accuracy is improved using variational mode decomposition (VMD). Numerical experiments have been carried out based on oil chromatographic data of power transformers from the Chongqing grid to validate effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-031ed608a49c41fca859cdd33ba25b60 |
| institution | DOAJ |
| issn | 2096-0042 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | China electric power research institute |
| record_format | Article |
| series | CSEE Journal of Power and Energy Systems |
| spelling | doaj-art-031ed608a49c41fca859cdd33ba25b602025-08-20T03:02:54ZengChina electric power research instituteCSEE Journal of Power and Energy Systems2096-00422025-01-011141596160910.17775/CSEEJPES.2021.0488010026211Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning MethodWeixin Zhang0Changzheng Shao1Wei Huang2Bo Hu3Jiahao Yan4Kaigui Xie5Maosen Cao6Zhengze Wei7School of Electrical Engineering, Chongqing University,State Key Laboratory of Power Transmission Equipment Technology,Chongqing,China,400044School of Electrical Engineering, Chongqing University,State Key Laboratory of Power Transmission Equipment Technology,Chongqing,China,400044School of Electrical Engineering, Chongqing University,State Key Laboratory of Power Transmission Equipment Technology,Chongqing,China,400044School of Electrical Engineering, Chongqing University,State Key Laboratory of Power Transmission Equipment Technology,Chongqing,China,400044School of Electrical Engineering, Chongqing University,State Key Laboratory of Power Transmission Equipment Technology,Chongqing,China,400044School of Electrical Engineering, Chongqing University,State Key Laboratory of Power Transmission Equipment Technology,Chongqing,China,400044School of Electrical Engineering, Chongqing University,State Key Laboratory of Power Transmission Equipment Technology,Chongqing,China,400044School of Electrical Engineering, Chongqing University,State Key Laboratory of Power Transmission Equipment Technology,Chongqing,China,400044Power transformers, as essential equipment for electricity transmission, may fail due to insulation degradation. Predicting the failure rate of power transformers precisely is beneficial to decision-making. Currently, uncertainties of the prediction have not been deeply discussed. Besides, prediction accuracy is not high enough. This paper proposes a decomposition-based Bayesian deep learning (BDL) method to predict the failure rate of power transformers. Both the model uncertainty related to distribution of the model's weights and the inherent uncertainty associated with random noise can be captured by BDL. Uncertainties of prediction results are depicted with confidence intervals. Moreover, prediction accuracy is improved using variational mode decomposition (VMD). Numerical experiments have been carried out based on oil chromatographic data of power transformers from the Chongqing grid to validate effectiveness of the proposed method.https://ieeexplore.ieee.org/document/10026211/Bayesian deep learningdissolved gas analysisfailure rate predictionlong short-term memoryvariational mode decomposition |
| spellingShingle | Weixin Zhang Changzheng Shao Wei Huang Bo Hu Jiahao Yan Kaigui Xie Maosen Cao Zhengze Wei Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method CSEE Journal of Power and Energy Systems Bayesian deep learning dissolved gas analysis failure rate prediction long short-term memory variational mode decomposition |
| title | Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method |
| title_full | Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method |
| title_fullStr | Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method |
| title_full_unstemmed | Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method |
| title_short | Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method |
| title_sort | failure rate prediction of a power transformer a decomposition based bayesian deep learning method |
| topic | Bayesian deep learning dissolved gas analysis failure rate prediction long short-term memory variational mode decomposition |
| url | https://ieeexplore.ieee.org/document/10026211/ |
| work_keys_str_mv | AT weixinzhang failureratepredictionofapowertransformeradecompositionbasedbayesiandeeplearningmethod AT changzhengshao failureratepredictionofapowertransformeradecompositionbasedbayesiandeeplearningmethod AT weihuang failureratepredictionofapowertransformeradecompositionbasedbayesiandeeplearningmethod AT bohu failureratepredictionofapowertransformeradecompositionbasedbayesiandeeplearningmethod AT jiahaoyan failureratepredictionofapowertransformeradecompositionbasedbayesiandeeplearningmethod AT kaiguixie failureratepredictionofapowertransformeradecompositionbasedbayesiandeeplearningmethod AT maosencao failureratepredictionofapowertransformeradecompositionbasedbayesiandeeplearningmethod AT zhengzewei failureratepredictionofapowertransformeradecompositionbasedbayesiandeeplearningmethod |