机械与电子2025，Vol.43Issue(10)：26-33,8.
基于SVMD和EVO-PNN模型的变压器绕组故障诊断
Transformer Winding Fault Diagnosis Based on Successive Variational Mode Decomposition and IEVO-PNN Model
摘要
Abstract
A transformer winding fault diagnosis method based on the combination of successful varia-tional mode decomposition(SVMD)and energy valley optimization algorithm optimized probabilistic neu-ral network(EVO-PNN)is proposed to address the problem of difficulty in accurately identifying differ-ent weak faults in power transformer windings.Firstly,SVMD is used to extract preliminary feature quan-tities of the original vibration signal of the transformer,and high correlation modal components are selected through the correlation coefficient method.The multi-scale fuzzy entropy values of the selected modal components are calculated to construct a dataset of different state feature data of the transformer winding;secondly,a method for optimizing the smoothing factor of PNN using EVO diagnosisalgorithm is pro-posed,and a transformer winding fault diagnosis model based on SVMD and EVO-PNN is established;fi-nally,using a S13-M-500/10 transformeras the experimental object,PNN,BA-PNN,GOA-KELM,WOA-SVM,and the proposed method are used to diagnose and identify different types of winding faults.The experimental results show that the proposed diagnostic model has a high diagnostic accuracy,with an overall recognition accuracy of 99.3％.关键词
变压器绕组/逐次变分模态分解/能量谷优化算法/概率神经网络/故障诊断Key words
transformer winding/successive variational mode decomposition/energy valley optimization algorithm/probabilistic neural network/fault diagnosis分类
动力与电气工程引用本文复制引用
王子伟,徐天乐,郑智燊,何信林,岳健国,柳宏斌..基于SVMD和EVO-PNN模型的变压器绕组故障诊断[J].机械与电子,2025,43(10):26-33,8.基金项目
中国华能集团有限公司科技项目(HNKJ22-H88) （HNKJ22-H88）
