基于最小二乘支持向量机的改进型GIS局部放电识别方法

利用最小二乘支持向量机（1east square-support vector machine，LS．SVM）的方法识别气体绝缘组合电器局部放电的类型。在信号的快速分类后利用相位分布的局部放电特征谱图的特征参数作为LS．SVM识别放电类型的依据；信号快速分类处理部分主要包括信号时间一频率特性提取部分和模糊C-均值聚类2...

An Improved Approach to Recognize Partial Discharge in GIS Based on Minimum Least Square-Support Vector Machine

The approach of minimum least square-support vector machine(LS-SVM) is used to recognize the type of partial discharge(PD) occurred in gas insulated switchgear(GIS).After rapid classification of signals,the characteristic parameters of spectrogram of PD characteristics based on phase distribution is used as the foundation to recognize PD type by LS-SVM.The fast classification processing of signals mainly includes two parts: the extraction of time-frequency characteristic of signals and fuzzy C-means clustering,and they divide the group of time-frequency points into several signal subgroups consisting of similar signals.Results of simulation tests show that the proposed method can effectively cope with complex occasions of equipment status and can effectively evade the defects of traditional neural network such as neural recognition network is greatly affected by initial value,too high dimensions of neural network,and so on.