电测与仪表2025，Vol.62Issue(3)：190-197,8.DOI:10.19753/j.issn1001-1390.2025.03.023
基于自适应VMD和优化DFNN的剩余电流识别
Residual current recognition based on adaptive VMD and optimized DFNN
摘要
Abstract
In order to realize rapid fault recognition of residual current device(RCD)and improve power safety,a fault residual current recognition method(AVMD-DFNN)based on adaptive variational modal decomposition(AVMD)and optimal dynamic fuzzy neural network(DFNN)is proposed.The decomposition parameters of VMD are determined adaptively by empirical mode decomposition(EMD)to realize the de-noising of the residual current signal.The characteristic parameters of residual current signal are extracted and used as the classification index of DFNN to recognize the type of residual current fault after the dimensionality reduction process.The DFNN is opti-mized by the minimum output method to remove the redundant fuzzy rule functions,so as to realize the rapid fault recognition of RCD.The simulation results show that AVMD-DFNN has high recognition accuracy and speed,which provides a theoretical reference for the development of new adaptive residual current devices.关键词
剩余电流/动态模糊神经网络/变分模态分解/故障识别Key words
residual current/dynamic fuzzy neural network/variational mode decomposition/fault recognition分类
动力与电气工程引用本文复制引用
张祥珂,王雅静,窦震海,白云鹏,王玮..基于自适应VMD和优化DFNN的剩余电流识别[J].电测与仪表,2025,62(3):190-197,8.基金项目
国家自然科学基金资助项目(52077221) （52077221）
山东省自然科学基金项目(ZR2020MF124) （ZR2020MF124）
