On-Line Trained Adaptive Neuro-Fuzzy Inference System for Distance Relay of Transmission line Protection

This paper presents a new distance relay technique for transmission line protection by using well known control technique; Adaptive Neuro-Fuzzy Inference System (ANFIS). The ANFIS can be viewed either as a fuzzy system, a neural network or fuzzy neural network FNN. The structure is seen as a neural network for training and a fuzzy viewpoint is utilized to gain insight into the system and to simplify the model. The integration with neural network technology enhances fuzzy logic systems on learning capabilities. The integration with neural network technology enhances fuzzy logic systems on learning capabilities. 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 technique is accomplished by one ANFIS that achieves accurate and fast estimation of distance to fault from the relay point. The normalized positive sequence impedance of the three phases are considered as inputs to the network The input data of the ANFIS were 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 location of faults for different fault conditions in fault inception time, fault impedance, fault distance and fault types