The application of chaos support vector machines in transformer fault diagnosis

Abstract

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. Experiment showed that by adopting facture extraction with KMC, the diagnosis information was concentrated and the consuming in parameter determination was solved effectively. On the other hand, chaos optimization enhanced model extension ability perfectly. Moreover, the presented method enabled to detect transformer faults with a high correct judgment rate, and can be used as an automation approach for diagnosis under condition of small samples.