Fault Classification of Phase to Phase Fault in Six Phase Transmission Line using Haar Wavelet and ANN

This paper presents a fault classification technique based on Haar Wavelet Transform (WT) and Artificial Neural Network (ANN) for six phase transmission line against phase to phase faults. The approximation & detailed coefficients of voltage & current signals are extracted using Haar WT. The standard deviation (SD) of approximated coefficient of voltage & current samples is used as input to the neural network for classification purpose. Six phase transmission line is modeled and the proposed protection technique has been developed using the Simulink (R) and Simpowersystem (R) toolboxes of MATLAB (R) 7.01. The effect of variation of fault parameters such as fault distance location, resistance and inception angle are also considered. The simulation result of Haar WT and ANN based fault classifier is presented in this paper. This has been discovered that the proposed method classifies all types of phase-to-phase fault accurately. Thus simulation results demonstrate the suitability and effectiveness of the proposed algorithm.