混沌支持向量机在变压器局部放电检测中的应用

针对变压器故障诊断中缺少实际典型故障样本的问题,提出了混沌支持向量机(CSVMs)变压器局部放电检测方法。该方法采用K均值聚类(KMC)对变压器油中5种特征气体样本进行预选取作为特征向量,输入到混沌优化多分类支持向量机中进行训练,建立CSVMs诊断模型,实现对故障样本的诊断分类。实例分析表明,KMC算法浓缩了故障信息,有效地解决了确定模型参数时耗时巨大的问题;混沌优化较好地提高了模型的推广能力。该方法在有限样本情况下,能够达到较高的故障正判率,满足变压器故障自动诊断的目的。

The Application of Chaos Support Vector Machines in Transformer Partial Discharge Measurents

Due to the lack of typical damage samples in the transformer fault diagnosis,a new method based on chaos support vector machines(CSVMs) was proposed.According to the method,the five characteristic gases dissolved in transformer oil were extracted by the K-means clustering(KMC) method as feature vectors,which were input to chaotic optimal multi-classified SVMs for training.Then the CSVMs diagnosis model was established to implement fault samples classification.The experiment shows that by adopting facture extraction with KMC,the diagnosis information is concentrated and the time-consuming in parameter determination is solved effectively.On the other hand,chaos optimization better enhanced model extension ability.Moreover,the presented method enables to detect transformer faults with a higher correct judgment rate,and can be used as an automation approach for diagnosis under condition of small samples.