高压电器2026，Vol.62Issue(3)：47-60,14.DOI:10.13296/j.1001-1609.hva.2026.03.007
基于优化VMD-TCN-LSTM的变压器油中溶解气体预测
Prediction of Dissolved Gas in Transformer Oil Based on Optimized VMD-TCN-LSTM
摘要
Abstract
As for the such complex characteristics as both long-term trends and short-term subtle fluctuations of non-stationary dissolved gas sequences in transformer oi,the GSSA-VMD model is formed by integrating the golden sine algorithm(GSA)-optimized sparrow search algorithm(SSA)with variational mode decomposition(VMD).The origi-nal dissolved gas sequences in transformer oil are decomposed by GSSA-VMD and a set of stationary modal compo-nents is finally obtained.Then,for accurately predicting the long-term trends and short-term fluctuations of gas se-quences of transformer,in this paper the temporal convolutional network(TCN)and long short-term memory(LSTM)are combined,which is further combined with GSSA-VMD to form a hybrid prediction model for dissolved gas con-tent in transformer oil.Finally,in this paper the dissolved gas CO2 in teranformer oil is selected for experimental verification.It is concluded by the comparative experiments with VMD-TCN-LSTM,EMD-TCN-LSTM,and GSSA-VMD-LSTM models that the hybrid prediction model proposed in the paper achieves the best performance,with a mean absolute percentage error(MAPE)of 0.71%and a root mean square error(RMSE)of 9.04 μL/L.关键词
油中溶解气体/组合预测模型/变压器/变分模态分解Key words
dissolved gas in oil/combination prediction model/transformer/variational mode decomposition引用本文复制引用
代浩,胡东,杨童亮,付强,杨勇,唐超,谭为民..基于优化VMD-TCN-LSTM的变压器油中溶解气体预测[J].高压电器,2026,62(3):47-60,14.基金项目
国家自然科学基金(51977179). Project Supported by National Natural Science Foundation of China(51977179). （51977179）
