中国电力2026，Vol.59Issue(8)：49-60,12.DOI:10.11930/j.issn.1004-9649.202604070
基于双尺度时序卷积网络和残差Bootstrap方法的电流互感器测量误差区间预测
Interval prediction of current transformer measurement error based on DTCN and residual Bootstrap method
摘要
Abstract
To address the problems that the ratio-error series of current-transformers,affected by environmental disturbance,operating condition variations and random noise,exhibit nonstationary and multi-scale characteristics,and it is difficult to balance point prediction and interval estimation,this paper proposes an interval forecasting method integrating complete ensemble empirical mode decomposition(CEEMD),dual-scale temporal convolutional network(DTCN)and bias-corrected residual bootstrap(BCRB).Firstly,The CEEMD is adopted to decompose and reconstruct the original ratio-error series to suppress high-frequency noise and mode mixing.Secondly,the DTCN is used to extract short-term fluctuation and long-term drift features to realize point prediction of ratio errors.Finally,prediction intervals under different confidence levels are constructed based on the residual bootstrap method.Case studies show that the proposed method achieves a high interval coverage probability at the confidence level of 0.99.关键词
电流互感器/测量误差/完全集合经验模态分解/双尺度时序卷积网络/区间预测Key words
current transformers/measurement error/complete ensemble empirical mode decomposition/dual-scale temporal convolutional network/interval prediction引用本文复制引用
朱何荣,李少东,赵森林,卢为,邓劲东,李振华..基于双尺度时序卷积网络和残差Bootstrap方法的电流互感器测量误差区间预测[J].中国电力,2026,59(8):49-60,12.基金项目
This work is supported by National Key Research and Development Program of China(No.2023YFB2405903). 国家重点研发计划资助项目(2023YFB2405903). （No.2023YFB2405903）
