Accurate Hybrid Method for Rapid Fault Detection, Classification and Location in Transmission Lines using Wavelet Transform and ANNs

The present paper presents an accurate hybrid framework capable to rapidly detect, classify & locate short-circuit faults on transmission lines. The proposed algorithm has employed the values resulted from each three-phase currents wavelet transform in order to obtain instantaneous fault detection. Singling out short-circuit faults based on the measured voltage waveforms and three-phase current is done when fault events occur in power transmission lines. The energy derived from three-phase currents and three-phase voltages wavelet transform has been used as the classification algorithm input. Then fault location has been activated as the result of fault classification method. Combining the methods such as multilevel wavelet transform, multilayer perceptron neural network in a set has been utilized to determine shortcircuit fault type and location at the moment of occurrence. The accuracy and superiority of the present paper derived results due to the fundamental wavelet transform concepts as an excellent feature extractor have been compared with those of another paper exploiting Fourier transform.