首页|基于CEEMDAN和TCN的变压器油中溶解气体含量预测

基于CEEMDAN和TCN的变压器油中溶解气体含量预测

扫码查看
原文链接
  • 万方数据
  • 维普
准确预测油中溶解气体含量的变化趋势,对变压器的状态评价和寿命评估有着积极的作用.为了提高油中溶解气体预测的准确性,文中提出一种基于自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和时间卷积网络(time convolution network,TCN)的油中溶解气体预测方法.首先,通过CEEMDAN方法将油中溶解气体含量的原始序列分解为多个本征模态分量,并将其中的稳定分量与非稳定分量分离;其次,对本征模态分量分别建立TCN并预测未来趋势变化;最后,叠加TCN对各个本征模态分量的预测结果,重构得到原始序列的预测结果.实例分析表明,该预测方法的均方根误差、平均绝对误差、最大误差分别为1.01μL/L、1.53 μL/L、5.54 μL/L,相较于未采用CEEMDAN算法时分别减小了 53.47％、41.18％、13.36％;在使用CEEMDAN的情况下,对比常用的递归神经网络,3种误差均最小.且对比现有油中溶解气体预测方法,文中提出的油中溶解气体预测方法具有更高的预测精度,可以为制定状态检修策略提供更有效的支撑.
Prediction of concentration for dissolved gas in oil based on CEEMDAN and TCN
Accurately predicting the concentration trend of dissolved gas in oil has a positive effect on the evaluation of transformer status and life assessment.In order to improve the accuracy of dissolved gas in oil prediction,a dissolved gas in oil prediction method based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and time convolution network(TCN)is proposed in this paper.Firstly,the CEEMDAN method is used to decompose the original sequence of dissolved gas in oil into multiple intrinsic mode functions(IMFs),separating the stable and unstable IMFs.Secondly,TCN are established for IMFs,then predictions based on these trained TCNs are made.Finally,the prediction results of IMFs are overlaid to reconstruct the prediction results of the original sequence.Analysis in this paper shows that the root mean square error,mean absolute error and maximum error of the prediction method are 1.01 μL/L,1.53 μL/L,5.54 μL/L respectively,which are reduced by 53.47％,41.18％,13.36％compared to the case without using the CEEMDAN.When using CEEMDAN,the three errors are the smallest compared to commonly used recurrent neural networks.The proposed dissolved gas in oil prediction method has higher prediction accuracy and can provide more effective support for condition based maintenance strategy.

dissolved gas in oiltransformercomplete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)time convolution network(TCN)time series forecastingcondition based maintenance

张文乾、刘金凤、江军、赵旭峰、范利东

展开 >

南京航空航天大学(江苏省新能源发电与电能变换重点实验室),江苏南京 211106

南京航空航天大学经济与管理学院,江苏南京 211106

杭州钱江电气集团股份有限公司,浙江杭州 311243

油中溶解气体 变压器 自适应噪声完备集合经验模态分解(CEEMDAN) 时间卷积网络(TCN) 时间序列预测 状态检修

国家自然科学基金

52177150

2024

10.12158/j.2096-3203.2024.03.020

电力工程技术
江苏省电力公司 江苏省电机工程学会

电力工程技术

CSTPCD北大核心
影响因子：0.969
ISSN：2096-3203
年,卷(期)：2024.43(3)
  • 16