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Nowadays, power supply has become a business commodity. The quality and reliability of power needs to be maintained in order to obtain optimum performance. Therefore, it is extremely important that transmission line faults from various sources be identified accurately, reliably and be corrected as soon as possible. In this paper, a comparative study of the performance of Fourier transform and wavelet transform based methods combined with Neural Network (NN) for location estimation of faults on high voltage transmission lines is presented. A new location method is proposed for decreasing training time and dimensions of NN. The proposed algorithms are based on Fourier transform analysis of fundamental frequency of current and voltage signals in the event of a short circuit on a transmission line. Similar analysis is performed on transient current and voltage signals using multi-resolution Daubchies-9 wavelet transform, and comparative characteristics of the two methods are discussed.

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