CAJ | 학술논문

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大部分，它们把信号的时间一频率点群分为由若干具有相似信号组成的信号子群。仿真实验表明该方法可有效地应对设备情况复杂的场合且有效回避传统神经网络识别受初始值影响较大、维数过高等一系列问题。
이용최소이승지지향량궤（1east square-support vector machine，LS．SVM）적방법식별기체절연조합전기국부방전적류형。재신호적쾌속분류후이용상위분포적국부방전특정보도적특정삼수작위LS．SVM식별방전류형적의거；신호쾌속분류처리부분주요포괄신호시간일빈솔특성제취부분화모호C-균치취류2대부분，타문파신호적시간일빈솔점군분위유약간구유상사신호조성적신호자군。방진실험표명해방법가유효지응대설비정황복잡적장합차유효회피전통신경망락식별수초시치영향교대、유수과고등일계렬문제。
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.