CMAC-based fault diagnosis of power transformers

Abstract

Dissolved gas analysis (DGA) is one of most useful techniques to detect the incipient faults of power transformer. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational natures. In this paper, a novel CMAC-based method is proposed for the fault diagnosis of power transformers. By introducing the IEC std. 599 to generate the training data, and using the characteristic of self-learning and generalization, like the cerebellum of human being, a CMAC-based fault diagnosis scheme enables a powerful, straightforward, and efficient fault diagnosis. With application of this scheme to published transformers data, the diagnoses demonstrate the new scheme with high accuracy and high noise rejection abilities. Moreover, the results also proved the ability of multiple incipient faults detection.