A combined adaptive network and fuzzy inference system (ANFIS) approach for overcurrent relay system

Accurate models of overcurrent (OC) relays with inverse time relay characteristics play an important role for the coordination of power system protection schemes. Conventional OC relay modelling using techniques like system identification, parameter estimation, direct data storage and software gave only approximate models. Hence, in this paper a new method for modelling OC relay characteristics curves based on a combined adaptive network and fuzzy inference system (ANFIS) is proposed. In this method, OC relay modeling is done using ANFIS for two types of OC relays (RSA20 and CRP9 with different types and various numbers of membership functions to bring out the optimal design. The simulated results are compared with the published results of analytical method and fuzzy model systems. The results obtained are quite encouraging and will be useful as an effective tool for modelling OC relays.

[1]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[2]  M. S. Sachdev,et al.  A technique for generating computer models of microprocessor-based relays , 1997, IEEE WESCANEX 97 Communications, Power and Computing. Conference Proceedings.

[3]  Hossein Kazemi Karegar,et al.  A new method for overcurrent relay (O/C) using neural network and fuzzy logic , 1997, TENCON '97 Brisbane - Australia. Proceedings of IEEE TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications (Cat. No.97CH36162).

[4]  Chul-Hwan Kim,et al.  A Novel Algorithm for Fault Classification in Transmission Lines Using a Combined Adaptive Network and Fuzzy Inference System , 2003 .

[5]  Tarlochan S. Sidhu,et al.  Software models for relays , 2001 .

[6]  Alessandro Ferrero,et al.  A fuzzy-set approach to fault-type identification in digital relaying , 1994 .

[7]  M. S. Sachdev,et al.  Computer representation of overcurrent relay characteristics: IEEE committee report , 1989 .

[8]  Mo-Yuen Chow,et al.  A Methodology Using Fuzzy Logic to Optimize Feedforward Artificial Neural Network Configurations , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[9]  B. Kulicke,et al.  Neural network approach to fault classification for high speed protective relaying , 1995 .

[10]  M. S. Sachdev,et al.  Modelling relays for use in power system protection studies , 2001 .

[11]  Huisheng Wang,et al.  Fuzzy-neuro approach to fault classification for transmission line protection , 1998 .

[12]  Jyh-Shing Roger Jang,et al.  Self-learning fuzzy controllers based on temporal backpropagation , 1992, IEEE Trans. Neural Networks.

[13]  Hossein Askarian Abyaneh,et al.  A flexible approach for overcurrent relay characteristics simulation , 2003 .

[14]  A. Bernieri,et al.  A neural network approach for identification and fault diagnosis on dynamic systems , 1993 .

[15]  M. S. Sachdev,et al.  Design, implementation and testing of an artificial neural network based fault direction discriminator for protecting transmission lines , 1995 .

[16]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..