基于EEMD能量矩与ISSA-SVM算法的GIS局部放电类型识别方法

Partial discharge identification method in GIS based on EEMD energy moment and ISSA-SVM algorithm

为有效识别气体绝缘开关组合电器(gas insulated switchgear,GIS)局部放电(partial discharge,PD)类型,进而保障设备安全稳定运行,提出了一种基于集合模态分解(ensemble empirical mode decomposition,EEMD)联合能量矩与改进麻雀群搜索算法优化支持向量机(improved sparrow search algorithm-support vector machines,ISSA-SVM)算法的GIS局部放电类型识别方法。首先搭建能产生4种局部放电类型效果的GIS局部放电实验平台,以获取4种局部放电信号,然后利用EEMD联合能量矩算法分别对4种局部放电信号进行模态分解与特征向量提取,最后利用经ISSA算法优化后的SVM算法对GIS局部放电类型进行识别。实验结果表明,所提方法可有效识别GIS不同局部放电类型,且较PSO-SVM与SSA-SVM算法识别精度分别提高了16.7%与8.5%,验证了所提GIS局部放电类型识别方法的有效性以及优越性。

In order to identify PD types in GIS effectively and ensure the safe and stable operation of equipment,a PD type in GIS identification method based on EEMD energy moment and ISSA-SVM algorithm is proposed.Firstly,a GIS partial discharge experiment platform that can produce four PD effects is built to obtain four PD signals.Then,EEMD and energy moment are used to decompose the modes and extract the feature vectors of the four PD signals.Finally,ISSA-SVM algorithm is used to identify GIS PD types.Experiment results show that the proposed method can identify different PD types in GIS effectively,and the recognition accuracy is improved by 16.7%and 8.5%respectively compared with PSO-SVM and SSA-SVM algorithm.The effectiveness and superiority of the proposed PD type identification method in GIS are verified by the experiment.