A Wavelet-based Adaptive Neuro Fuzzy Inference System Approach to Fault Classification for High Speed Protective Relaying

Protecting transmission lines is very important for safeguard of the power system. There are many methods for protecting the power system from faults conventional methods and heuristic methods. The results obtained by conventional methods are difficulty because of the complexity of the system model, the lack of knowledge of its parameters, the large amount of information to be processed and the difficulty in taking into consideration any system variation as the rules are fixed. The objective of this paper to overcome the problems of conventional methods to develop a model for real-time fault detection and classification in power transmission systems using wavelet-based Adaptive Neuro Fuzzy Inference System approach. Only the three line currents are utilized to detect fault types such as LG, LL and LLG, and then to define the faulty line. In this model, the wavelet transform is used to detect the fault and Adaptive Neuro Fuzzy Inference System approach is used to find the nature of fault [1]. MATLAB programming and Simulink are used to test the proposed algorithm and to identify the effectiveness of the proposed technique.