Differential protection schemes for classification of fault detection between external fault and internal winding fault in transformer using probabilistic neural network

Abstract

This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and probabilistic neural network (PNN) for discriminating between external fault and internal winding fault in power transformer. The coefficients of the first scale from the DWT that can detect fault are investigated. The maximum coefficients details (cD1) from DWT in first scale at ¼ cycle of phase A, B, C and zero sequence for post-fault differential current waveforms have been used as an input for the training process of the PNN in a decision algorithm. Various cases studies based on Thailand electricity transmission and distribution systems have been investigated so that the algorithm can be implemented. The results show that the proposed algorithm is capable of performing the fault detection with satisfactory accuracy.