基于改进变分模态分解的有载分接开关机械状态监测

为提高变压器有载分接开关(On-Load Tap-Changer,OLTC)机械状态智能诊断水平,提出了基于改进变分模态分解(Improved Variational Mode Decomposition,IVMD)-权重散度的OLTC机械状态特征提取方法,以及和声搜索算法(Harmony Search,HS)优化相关向量机(Relevance Vector Machine,RVM)的故障分类方法.本文进行模拟实验测得了多组不同工况下的OLTC机械振动信号,通过IVMD算法将振动信号分解为一系列有限带宽本征模态函数(Intrinsic Mode Function,IMF),计算IMF分量与原始振动信号的K-L散度(Kullback-Leibler Divergence,K-L),再乘上权重系数得到权重散度,以权重散度来表征多组OLTC机械振动信号的时频域复杂度.同时构建了RVM多分类模型,并通过和声搜索算法对RVM的核函数选择进行了优化,有效地实现了对于权重散度的分类.实验与数据分析结果表明,本文所提综合诊断方法精度较高,可准确提取机械故障特征,能够为OLTC智能故障诊断提供必要的参考.
In order to improve the intelligent diagnosis level of an on-load tap-changer (OLTC) mechanical condition,a feature extraction method was proposed based on improved variational mode decomposition (IVMD) and weight divergence. The harmony search (HS) algorithm was used to optimize the parameter selection of the relevance vector machine (RVM). The mechanical vibration signals of OLTC under different conditions were measured by simulation experiments. The OLTC vibration signals were then decomposed into a series of finite-bandwidth intrinsic mode function (IMF) by IVMD. Next,Kullback–Leibler divergence (K-L divergence) of the IMF and original vibration signal was calculated. The K-L divergence was multiplied by the weight coefficient to obtain the weight divergence,which represented the time-frequency domain complexity of the OLTC mechanical vibration signals. Simultaneously,the multi-classification model of RVM was constructed. The selections of kernel function parameters were optimized by HS,and the classification of weight divergence was realized effectively. The experimental and data analysis results show that the proposed integrated model exhibits high fault diagnosis accuracy. This model can accurately extract the characteristics of mechanical condition,and provide reference for the practical OLTC intelligent fault diagnosis.