电力系统保护与控制2026，Vol.54Issue(11)：37-48,12.DOI:10.19783/j.cnki.pspc.251368
基于混沌进化优化算法优化VMD的小波去噪与支持向量机的漏磁信号早期故障识别
Incipient fault identification of leakage magnetic-flux signals using wavelet denoising and support vector machine with chaos evolution-optimized VMD
摘要
Abstract
To address the problems of strong noise contamination and weak feature representation in leakage magnetic field signals for early transformer fault identification,as well as the reliance of variational mode decomposition(VMD)parameters on manual tuning and the tendency of traditional intelligent optimization methods to fall into local optima,an early fault identification approach is proposed by optimizing VMD using chaotic evolution optimization(CEO)and integrating wavelet threshold(WT)denoising with support vector machine(SVM)classification.Taking envelope entropy minimization as the objective function,CEO is employed to adaptively search for the optimal VMD parameters,including the mode number K and penalty factor α,thereby obtaining stable band-limited mode decomposition.On this basis,WT denoising is further applied to suppress residual narrowband periodic interference,enabling robust reconstruction of the leakage magnetic field signal.According to the axial distribution characteristics of the leakage magnetic field,a feature vector is constructed,and an RBF-kernel SVM is used to identify the type and location of six typical incipient faults.Results from ANSYS simulations and dynamic experiments based on fiber-optic magneto-optic measurement demonstrate that the envelope-entropy reduction achieved by the proposed CEO approach outperforms particle swarm optimization(PSO)and the firefly algorithm(FA).Under the same diagnostic framework,the proposed CEO-VMD-WT-SVM method achieves an accuracy exceeding 98％and exhibits superior overall performance compared with manual parameter selection,PSO,and FA,providing an efficient and practical solution for online early transformer fault diagnosis.关键词
变压器早期故障/混沌进化优化算法/变分模态分解/小波阈值法/支持向量机Key words
transformer early fault/chaotic evolution optimization/variational mode decomposition/wavelet threshold method/support vector machine引用本文复制引用
刘建锋,庞淳轩,李心茹,邓祥力..基于混沌进化优化算法优化VMD的小波去噪与支持向量机的漏磁信号早期故障识别[J].电力系统保护与控制,2026,54(11):37-48,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.51777119). 国家自然科学基金项目资助(51777119) （No.51777119）
