Recognition technology of winding deformation based on principal components of transfer function characteristics and artificial neural network

Abstract

In this paper, an intelligent identification method for winding deformation fault is proposed. The proposed method is composed of principal components of transfer function characteristics and an artificial neural network (ANN). A sequence of simulative deformation faults with different types, locations and extents are set on the winding of a 10kV transformer. The corresponding status transfer function is acquired with a winding deformation test method excited by M-Sequence. Zeros, poles and the variations of the transfer function are considered to be the features of the winding mechanical status. The principal components of feature are extracted and then used as input to a back-propagation ANN for fault recognition. The winding deformation faults are recognized using the ANN that has been trained and tested using the cross validation method. The results show that the classification method has the ability to simultaneously recognize the deformation faults with different types, locations and extents with high accuracy and is suitable for winding deformation diagnosis. The study presents an idea and a path to identify winding mechanical status intelligently though it conducts on a transformer.

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