Fault diagnosis of transformer based on principal component analysis and self-organizing map neural network

In order to effectively solve the problems such as fault with single features, carrying the lack of information resulting in poor effect of fault judgment in traditional transformer fault diagnosis method. In view of this, this paper will consider the insulation resistance of the core 12 fault symptoms and fault characteristic gases as the combination of fault characteristics, effectively solve the problem of the existence of the feature quantity; At the same time, the principal component analysis and self-organizing neural network combination is introduced to this paper, this paper puts forward a new method of transformer fault diagnosis based on principal component analysis and self-organizing neural network. Firstly, using the principal component analysis for the main components of 17 kinds of feature extraction, extracted features can effectively present the characteristics of 10 kinds of fault classification, on this basis, the 10 methods of feature extraction was obtained as input vector of self-organizing neural network and identification of fault types. The example analysis shows that this method has a good diagnostic effect, which can effectively improve the accuracy of fault diagnosis and verify the validity and feasibility of this method.