摘要
为了完成检测、维修等重要工作,针对变压器油中溶解气体浓度变化趋势预测的问题,文中将经验模态分解(Empirical Mode Decomposition,EMD)方法、编码器(Encoder)模块和长短时记忆(Long Short-Term Memory,LSTM)神经网络相结合,提出一种新颖的EMD-E-LSTM网络预测模型来实现油中溶解气体浓度的预测。对110 kV变压器油中溶解C2H6气体浓度预测算例结果表明,相较于E-LSTM预测方法、EMD-LSTM预测方法,所提EMD-E-LSTM网络预测结果的平均绝对百分比误差分别降低了22.23%和5.50%、均方根误差分别下降了18.18%和44.02%,而最大相对误差介于两者之间。所提方法对于其他溶解气体的预测精度也有不同程度的提高,展现出良好的应用前景。
For the purpose of predicting the change trend of dissolved gas concentration in transformer oil,and then completing important work such as detection and maintenance.In this paper,the empirical mode decomposition EMD method,Encoder module and the LSTM neural network are combined.A novel EMD-E-LSTM network prediction model is proposed to predict dissolved gas concentration in oil.The prediction results of dissolved C2H6 gas concentration in 110 kV transformer oil show that compared to the E-LSTM prediction method and the EMD-LSTM prediction method,the MAPE of the proposed EMD-ELSTM network prediction results have decreased by 22.23%and 5.50%,and the root⁃mean⁃square error has decreased by 18.18%and 44.02%,while the maximum relative error was between the two.The proposed method can also improve the prediction accuracy of other dissolved gases,showing a good application prospect.
作者
龙玉江
姜超颖
甘润东
吴建蓉
LONG Yujiang;JIANG Chaoying;GAN Rundong;WU Jianrong(Information Center of Guizhou Power Grid Company Limited,Guiyang 550003,China;Xidian University,Xi’an 710071,China;Guizhou Electric Power Research Institute,Guiyang 550003,China)
出处
《电子设计工程》
2025年第2期12-17,共6页
Electronic Design Engineering
基金
陕西省自然科学基础研究计划(2022JM-336)
南方电网有限责任公司科技项目(GZKJXM20200770)。
