高压电器2023，Vol.59Issue(12)：168-175,8.DOI:10.13296/j.1001-1609.hva.2023.12.020
基于CEEMDAN-SG-BiLSTM的变压器油中溶解气体体积分数预测
Prediction for Dissolved Gas Concentration in Power Transformer Oil Based on CEEMDAN-SG-BiLSTM
摘要
Abstract
The dissolved gas concentration in transformer oil can provide an important evidence for potential fault di-agnosis of transformer.For predicting the dissolved gas concentration in transformer oil more accurately,a combined prediction method of CEEMDAN and BiLSTM network is proposed.Firstly,for the issue of modal aliasing in EMD and lager reconstruction error in EEMD,the CEEMDAN is proposed.The gas sequences are decomposed to get the modal component and the high frequency fluctuation component is subjected to Savitzky-Golay(SG)filtering to weak-en the extreme value point of high frequency component and the noise interference.Then,the BiLSTM network is used to predict each component to improve the global feature extraction further.Finally,the prediction value of dis-solved gas concentration in the transformer oil is obtained by superposition and reconstruction of the prediction re-sults of each component.It is confirmed by the calculation that,compared with other modules,the proposed method is more accurate and its effectiveness is verified.关键词
变压器/油中溶解气体/添加自适应白噪声完全集合经验模态分解/Savitzky-Golay滤波/双向长短期记忆网络Key words
transformer/dissolved gas in oil/complete ensemble empirical mode decomposition with adaptive noise/Savitzky-Golay filter wave/bi-directional long short-term memory network引用本文复制引用
陈铁,陈一夫,李咸善,陈卫东,冷昊伟,陈忠..基于CEEMDAN-SG-BiLSTM的变压器油中溶解气体体积分数预测[J].高压电器,2023,59(12):168-175,8.基金项目
国家自然科学基金资助项目(51741907) （51741907）
梯级水电站运行与控制湖北省重点实验室开放基金(2019KJX08).Project Supported by National Natural Science Foundation of China(51741907),Open Fund of Key Laboratory for Operation and Control of Cascaded Hydropower Station in Hubei Province,China(2019KJX08). （2019KJX08）
