高压电器2025，Vol.61Issue(9)：81-91,11.DOI:10.13296/j.1001-1609.hva.2025.09.011
基于多分解策略和BO-LSTM的变压器油中溶解气体预测方法
Prediction Method of Dissolved Gas in Transformer Oil Based on Multi Decomposition Strategy and BO-LSTM
陈志勇 1杜江1
作者信息
- 1. 河北工业大学河北省电磁场与电器可靠性重点实验室,天津 300400||河北工业大学省部共建电工装备可靠性与智能化国家重点实验室,天津 300400
- 折叠
摘要
Abstract
For improving the prediction accuracy of dissolved gas concentration in oil and providing theoretical ba-sis for early fault warning and formulation of maintenance plans of transformer,in this paper a prediction model based on multi-decomposition strategy and Bayesian optimized long short-term memory neural network is proposed.Firstly,the original time series of dissolved gas in oil is decomposed by using the improved complete ensemble em-pirical mode decomposition with adaptive noise algorithmso as to obtain a series of subsequence components at dif-ferent time scales.Then,the high frequency IMF1 component,which is the most difficult to predict,is decomposed by variational mode decomposition algorithm to reduce it's nonlinearity and non-stationarity further.After that,the Bayesian optimized long short-term memory neural network is used to model all the subsequence components after second decomposition and performe single step prediction and recursive multi-step prediction.Finally,the predic-tion results of all components are superposed and reconstructed to obtain the final single step and multi-step predic-tion results.The results of examples show that the method proposed in this paper can effectively improve the predic-tion accuracy of dissolved gas concentration in oil.The proposed method,compared with the other six comparison models,exhibits the best performance in single step and multi-step prediction and its effectiveness is fully verified.关键词
油中溶解气体/时间序列预测/多分解策略/长短期记忆神经网络/贝叶斯优化Key words
dissolved gas in oil/time series prediction/multiple decomposition strategies/long short-term memo-ry neural networks/Bayesian optimization引用本文复制引用
陈志勇,杜江..基于多分解策略和BO-LSTM的变压器油中溶解气体预测方法[J].高压电器,2025,61(9):81-91,11.
