Adaptive Neuro Fuzzy Inference System (ANFIS) For Fault Classification in the Transmission Lines

This paper introduces the application of Adaptive Neuro-Fuzzy Inference System (ANFIS) for fault classification in transmission lines. It will be addressed clearly in this paper. The ANFIS can be viewed either as a fuzzy system, a neural network or fuzzy neural network (|FNN). This paper is integrating the learning capabilities of neural network to the robustness of fuzzy logic systems in the sense that fuzzy logic concepts are embedded in the network structure. It also provides a natural framework for combining both numerical information in the form of input/output pairs and linguistic information in the form of IF–THEN rules in a uniform fashion. The proposed algorithm is achieved by the intelligent scheme ANFIS. This intelligent scheme is used to classify the fault type and deduce if it is single phase to ground, phase to phase, double phase to ground, or three phases. The input data of the ANFIS are firstly derived from the fundamental values of the voltage and current measurements after making Fourier transform. Computer simulation results are shown in this paper and they indicate this approach can be used as an effective tool for classification of faults for different fault conditions in fault inception time, fault impedance, fault distance and fault type.