基于混合分解和IDBO-TCN的变压器油中溶解气体含量预测

付文龙; 祝鑫锋; 吴川锋; 朱福宝; 邵孟欣; 赵一博

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基于混合分解和IDBO-TCN的变压器油中溶解气体含量预测

更新时间：2025-03-24

- 基于混合分解和IDBO-TCN的变压器油中溶解气体含量预测
- Prediction of Dissolved Gas Volume Fraction in Transformer Oil Based on Mixture Decomposition and IDBO-TCN
- 默认刊物名称 2025年
- 作者机构：
  
  1.三峡大学梯级水电站运行与控制湖北省重点实验室,湖北省宜昌市443000
  2.三峡大学电气与新能源学院,湖北省宜昌市443000
- 作者简介：
  
  [ "付文龙(1988—)，男，博士，副教授，主要从事电气设备状态监测与诊断研究" ]
- 基金信息：
  
  国家自然科学基金资助项目(51741907);国网吉林省电力有限公司科技项目(2023JBGS-08)
- DOI：
  中图分类号：
- 收稿日期：2024-07-25，
  
  修回日期：2025-03-13，
  
  录用日期：2025-03-13，
- 稿件说明：
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付文龙, 祝鑫锋, 吴川锋, 等. 基于混合分解和IDBO-TCN的变压器油中溶解气体含量预测[J/OL]. 默认刊物名称, 2025.

FU Wenlong, ZHU Xinfeng, WU Chuanfeng, et al. Prediction of Dissolved Gas Volume Fraction in Transformer Oil Based on Mixture Decomposition and IDBO-TCN[J/OL]. Moren journal, 2025.
付文龙, 祝鑫锋, 吴川锋, 等. 基于混合分解和IDBO-TCN的变压器油中溶解气体含量预测[J/OL]. 默认刊物名称, 2025. DOI：

FU Wenlong, ZHU Xinfeng, WU Chuanfeng, et al. Prediction of Dissolved Gas Volume Fraction in Transformer Oil Based on Mixture Decomposition and IDBO-TCN[J/OL]. Moren journal, 2025. DOI：

摘要

准确预测变压器油中溶解气体的变化规律和趋势是保证变压器安全可靠运行的关键。为此提出一种基于混合分解和IDBO-TCN的油中溶解气体含量预测方法。首先，利用自适应噪声完全集合经验模态分解对给定气体含量序列进行分解；然后，采用经验小波变换对分解出的最高频分量进行二次分解；进一步对所有分量建立时序卷积网络预测模型；同时提出改进蜣螂优化算法对TCN的初始学习率和卷积核大小进行优化；最后，将各分量的预测值累加，得到最终预测结果。通过油中溶解气体CH

、C

、H

和总烃的预测实例证明了所提方法的优越性。

Abstract

Accurate prediction of the change rule and trend of dissolved gas in transformer oil is crucial in ensuring the safe and reliable operation of transformer. For this purpose

a prediction method based on mixture decomposition and IDBO-TCN is proposed to predict dissolved gases content in transformer oil. Firstly

the given gas content sequence is decomposed by using complete ensemble empirical mode decomposition with adaptive noise. Subsequently

empirical wavelet transform is applied to perform a second decomposition of the highest frequency component. Then

temporal convolutional network is established for all components. Simultaneously

the improved dung beetle optimization is proposed to optimize the initial learning rate and convolution kernel size of TCN. Finally

predicted values of each component are accumulated to obtain the final prediction result. The advantages of the proposed method are demonstrated by predicting the dissolved gases CH

and total hydrocarbons in oil.

关键词

Keywords

references

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