Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method

Weixin Zhang; Changzheng Shao; Wei Huang; Bo Hu; Jiahao Yan; Kaigui Xie; Maosen Cao; Zhengze Wei

doi:10.17775/CSEEJPES.2021.04880

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method

Regular Papers | 更新时间：2026-10-15

- Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method
- Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method
- 中国电机工程学会电力与能源系统学报(英文) 2025年11卷第4期页码：1596-1609
- 作者机构：
  
  State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University,Chongqing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.04880
  中图分类号：
- 纸质出版：2025-07-01
- 稿件说明：
移动端阅览
Weixin Zhang, Changzheng Shao, Wei Huang, 等. Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method[J]. 中国电机工程学会电力与能源系统学报(英文), 2025,11(4):1596-1609.

Weixin Zhang, Changzheng Shao, Wei Huang, et al. Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method[J]. CSEE Journal of Power and Energy Systems, 2025, 11(4): 1596-1609.
Weixin Zhang, Changzheng Shao, Wei Huang, 等. Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method[J]. 中国电机工程学会电力与能源系统学报(英文), 2025,11(4):1596-1609. DOI： 10.17775/CSEEJPES.2021.04880.

Weixin Zhang, Changzheng Shao, Wei Huang, et al. Failure Rate Prediction of a Power Transformer: A Decomposition-Based Bayesian Deep Learning Method[J]. CSEE Journal of Power and Energy Systems, 2025, 11(4): 1596-1609. DOI： 10.17775/CSEEJPES.2021.04880.

摘要

Abstract

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.

关键词

Keywords

references

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

Very Short-Term Probabilistic Prediction for Regional Wind Power Generation Based on OPNPIs

Discrete Fourier Transformation Matrix for Global Sensitivity Analysis in Probabilistic Optimal Power Flow Calculation

Predictive Reliability Assessment of Distribution Grids with Residential Distributed Energy Resources

Mechanism of Eddy Current-induced Electromagnetic Force and Vibration Noise of Large-Capacity Transformer Oil Tanks

A Practical Planning Method of Gas-Fired Power Generator for Integrated Gas-Electricity Systems Considering Reliability Index