Neural network excitation control system for transient stability analysis of power system

This paper presents Artificial Neural Network (ANN) based controller model to simulate the Automatic Voltage Regulator (AVR) response for the transient stability analysis. Two different models are simulated considering single machine connected to an infinite bus (SMIB) to check the response of NN behaviour with conventional controller. Based on simulation results, it is found that NN controller gives better response by removing oscillations, while the both conventional AVR controllers show ripple and oscillations before reaching the steady state condition. The development of a neural network based software controller to simulate the automatic voltage regulator behavior for improving transient stability of power system.

[1]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[2]  Kuldip S. Rattan,et al.  Real-time tracking control of a DC motor using a neural network , 1995, Proceedings of the IEEE 1995 National Aerospace and Electronics Conference. NAECON 1995.

[3]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[4]  Charles Concordia,et al.  Concepts of Synchronous Machine Stability as Affected by Excitation Control , 1969 .

[5]  R. L. Hardy.  Multiquadric equations of topography and other irregular surfaces , 1971 .

[6]  T. G. Habetler,et al.  Identification and control of induction motors using artificial neural networks with random weight change training , 1996, Proceedings of IEEE. AFRICON '96.

[7]  Mukhtiar Ali Unar,et al.  Automatic steering of ships using neural networks , 1999 .

[8]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[9]  A. Walton.  A systematic method for the determination of the parameters of synchronous machines from the results of frequency response tests , 2000 .

[10]  A. Walton.  A systematic analytical method for the determination of the parameters of synchronous machines from the results of frequency response tests , 1999 .

[11]  Mukhtiar Ali Unar,et al.  Ship steering control using feedforward neural networks , 1999 .

[12]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[13]  H. H. Happ,et al.  Power System Control and Stability , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Yasuhiko Dote,et al.  Radial basis neural network adaptive controller for servomotor , 1993, ISIE '93 - Budapest: IEEE International Symposium on Industrial Electronics Conference Proceedings.

[15]  Johan A. K. Suykens,et al.  Artificial neural networks for modelling and control of non-linear systems , 1995 .

[16]  Ferber R. Schleif,et al.  Excitation ControltoImprove Powerline Stability , 1968 .

[17]  M. J. D. Powell,et al.  Radial basis functions for multivariable interpolation: a review , 1987 .

[18]  P. Kundur,et al.  Power system stability and control , 1994 .

[19]  George W. Irwin,et al.  Neural networks for control and systems , 1992 .

[20]  Kazuo Tanaka,et al.  An approach to stability criteria of neural-network control systems , 1996, IEEE Trans. Neural Networks.

[21]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.