Discriminating between External Short Circuit and Internal Winding Fault in Power Transformer using RBF Neural Networks

Jittiphong Klomjit, Atthapol Ngaopitakkul, Chaiyan Jettanasen, Chaichan Pothisarn, and Surakit Thongsuk


Transformer, RBF Neural Network, Winding Fault, Short Circuit


In the literature for fault detection, several decision algorithms have been developed to be employed in the protective relay. In previous research works, the behaviour analysis of signals is performed using DWT. The results obtained from the analysis will be useful in the development of a detected fault scheme for power transformer in this paper. This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and radial basis function neural network (RBFNN) for discriminating between external fault and internal winding fault of three-phase two-winding transformer. The DWT is employed for extracting the high frequency component contained in the post-fault differential current waveforms, and the coefficients of the first scale from the DWT that can detect fault are investigated as an input for the training pattern. Various cases studies based on Thailand electricity transmission and distribution systems have been investigated so that the algorithm can be implemented. Results show that the proposed technique is highly satisfactory.

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