电力系统保护与控制2025，Vol.53Issue(1)：104-113,10.DOI:10.19783/j.cnki.pspc.240363
声信号下基于双通道特征融合网络的电抗器故障诊断方法
Fault diagnosis method of reactor based on a dual-channel feature fusion network with an acoustic signal
摘要
Abstract
At present,the fault diagnosis methods of a dry reactor mainly focus on mechanical faults based on vibration signals.The fault type is singular,and there are problems such as difficulties with sensor installation.Thus,a dry reactor fault test platform based on an acoustic signal is built,and a variety of fault types are set up.To improve the accuracy of fault identification in small samples,a dry reactor fault diagnosis method based on a dual-channel feature fusion network is proposed.First,the one-dimensional time sequence is converted into two-dimensional image using Gramian angle field(GAF)encoding.Secondly,a two-channel parallel CNN-ResNet network structure is adopted,and an efficient channel attention(ECA)mechanism is introduced to obtain two-dimensional key information.Then the two-dimensional image features and one-dimensional time series features are extracted and fused.Finally,the source domain data is obtained based on finite element simulation,and the optimal network parameters of the target domain are obtained by the transfer learning method.Experimental comparison shows that the proposed method has stronger feature extraction ability than other methods,can significantly separate fault features,and the fault identification accuracy can reach 99.5%with small samples.It has good generalizability and convergence speed.关键词
电抗器/声信号/故障/特征Key words
reactor/acoustic signal/fault/features引用本文复制引用
孙抗,张浩,杨林,常亮,杨明..声信号下基于双通道特征融合网络的电抗器故障诊断方法[J].电力系统保护与控制,2025,53(1):104-113,10.基金项目
This work is supported by the National Natural Science Foundation of China(No.U1804143). 国家自然科学基金项目资助(U1804143) （No.U1804143）
河南省科技攻关计划项目资助(202102210092) （202102210092）
