基于缺失数据填补的变压器油中溶解气体体积分数预测

王振国; 宁飞; 袁宝; 贾飞; 贾毓彦

doi:10.13296/j.1001-1609.hva.2026.10.007

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基于缺失数据填补的变压器油中溶解气体体积分数预测

研究与分析 | 更新时间：2026-10-14

- 基于缺失数据填补的变压器油中溶解气体体积分数预测
- Prediction of Dissolved Gas Concentration in Transformer Oil Based on Missing Data Imputation
- 高压电器 2026年62卷第10期页码：59-67
- 作者机构：
  
  内蒙古电力（集团）有限责任公司乌海供电份公司，内蒙古乌海 016000
  华北电力大学电力工程系，河北保定 071003
- 作者简介：
  
  王振国（1973—），男，高级工程师，研究方向为变电设备在线监测与故障诊断（E-mail：554355184@qq.com）。
  宁飞（1989—），男，高级工程师，研究方向为高电压绝缘（通信作者）（E-mail：lzlq2602@163.com）。
- 基金信息：
  
  国家自然科学基金面上项目the General Program of the National Natural Science Foundation of China.(51877084)
- DOI：10.13296/j.1001-1609.hva.2026.10.007
  中图分类号：
- 收稿：2026-05-26，
  
  修回：2026-08-05，
  
  纸质出版：2026-10-16
- 稿件说明：
移动端阅览
王振国, 宁飞, 袁宝, 等. 基于缺失数据填补的变压器油中溶解气体体积分数预测[J]. 高压电器, 2026,62(10):59-67.

WANG Zhenguo, NING Fei, YUAN Bao, et al. Prediction of Dissolved Gas Concentration in Transformer Oil Based on Missing Data Imputation[J]. High Voltage Apparatus, 2026, 62(10): 59-67.
王振国, 宁飞, 袁宝, 等. 基于缺失数据填补的变压器油中溶解气体体积分数预测[J]. 高压电器, 2026,62(10):59-67. DOI： 10.13296/j.1001-1609.hva.2026.10.007.

WANG Zhenguo, NING Fei, YUAN Bao, et al. Prediction of Dissolved Gas Concentration in Transformer Oil Based on Missing Data Imputation[J]. High Voltage Apparatus, 2026, 62(10): 59-67. DOI： 10.13296/j.1001-1609.hva.2026.10.007.

摘要

油中溶解气体体积分数预测对于早期变压器故障诊断具有重要意义。针对当前油中溶解气体体积分数预测算法对数据完整性要求高，泛化能力弱等问题，以油浸式变压器为研究对象，提出一种基于缺失数据填补和改进卡尔曼滤波的油中溶解气体体积分数预测算法。首先以贝叶斯理论和高斯先验假设为基础，构建缺失数据填补的全概率模型，将缺失数据填补转化为求解模型参数最大似然估计问题，利用期望最大（expectation maximization，EM）算法完成模型求解，最终实现缺失数据填补。然后采用卡尔曼滤波进行气体体积分数预测，针对卡尔曼滤波预测结果易发散问题，引入BP神经网络并建立反馈机制，实现对预测结果的动态修正，提升预测精度。基于实际数据的试验结果表明，所提方法在数据缺失率为30%的情况下，填补结果的相关系数达到96.3%，相对于自回归移动平均（ARIMA）和极端随机树（extremely randomized trees，ERT）分别提升11.4%和5.7% 。同时，所提方法的预测精度较高，相对于长短时记忆神经网络（long short term memory，LSTM）和长短时记忆—卷积神经网络（long short term memory-convolution neural network，LSTM-CNN）方法预测结果的均方根误差（

RMSE

）和平均绝对误差（

MAPE

）分别降低28.5%、33.6%。

Abstract

The prediction of dissolved gas concentration in oil is of great significance for earlyfault diagnosis of transformer. To address the issues of high data integrity requirements and weak generalization ability in current prediction algorithms for dissolved gas concentration in oil

in this paper the oil immersed transformers is taken as the research object anda prediction algorithm for dissolved gas concentration in oil based on missing data imputation and an improved Kalman filtering is proposed. First

a full probability model for missing data imputation is constructed based on Bayesian theory and Gaussian prior assumption.The missing data imputation problem is transformed into solving the problem maximum likelihood estimation of model parameters

and the expectation

maximization(EM)algorithm is employed to solve the modle

ultimately achieving the imputation of missing data. Then

Kalman filtering is used for gas concentration prediction. To address the issue of divergence in Kalman filtering

a BP neural network is introduced and a feedback mechanism is established so to dynamically correct the prediction results and improve prediction accuracy. The experimental results based on actual data show under a data missing rate of 30%

thecorrelation coefficient of the imputation results achieved by the proposed dmethod reaches 96.3%

which is 11.4% and 5.7% higher than those of the autoregressiveintegrated moving average(ARIMA)and extremely randomized trees(ERT)

respectively. Meanwhile

the proposed method has a higher prediction accuracy

and the root mean square error(

RMSE

)and mean absolute error(

MAPE

)are reduced by 28.5% and 33.6% respectively compared to long short term memory(LSTM)and long short term memory-convolutional neural network(LSTM-CNN)methods.

关键词

Keywords

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