基于特征分类算法的GIS故障诊断方法研究

针对高压断路器操动机构故障监测问题,提出了一种基于核主成分分析和支持向量机的气体绝缘开关故障检测方法,利用核主成分分析对分(合)闸线圈电流波形的特征值进行降维,然后将降维后的特征值输入多类分类SVM进行故障诊断和分类。通过实际样本的实验,验证了算法的准确性和可靠性,并通过参数讨论,测算了最优的分类参数。

Gas Insulated Switch Fault Diagnosis Method Research Based on Characteristic Classification Algorithm

In allusion to the fault monitoring problem of high voltage circuit breaker operating mechanism, this paper raised a kind of fault detection method for gas insulated switch (GIS) based on kernel principal component analysis (KPCA) and support vector machine (SVM). The KPCA algorithm was used to reduce dimension of eigenvalue of coil current waveform, which was input multi-classified SVM. The practical sample experiment verifies the correctness and reliability of the algorithm, and the discussion is proposed to calculate the optimal parameter.