Wavelet Based Fault Detection, Classification and Location in Transmission Lines

This paper deals with the application of wavelet transforms for the detection, classification and location of faults on transmission lines. A global positioning system synchronizing clock is used to sample three phase voltage and current signals at both the ends of the transmission line over a moving window length of half cycle. The current signals are analyzed with Bior2.2 wavelet to obtain detail coefficients of single decompositions. Fault indices are calculated based on the sum of local and remote end detail coefficients, and compared with threshold values to detect and classify the faults. For estimation of fault location feed forward artificial neural networks are employed, which make use of third level approximate decompositions of the voltages and currents of local end obtained with Bior4.4 wavelet. Two types of neural networks are proposed, one for locating phase faults and the other for ground faults. The proposed algorithm is tested for different locations and types of faults as well as for various incidence angles and fault impedances. The algorithm is proved to be efficient and effective in detecting, classifying and locating faults.