Neural network identification of power system transfer functions

This paper describes an investigation into the use of a multilayered neural network for measuring the transfer function of a power system for use in power system stabilizer (PSS) tuning and assessing PSS damping. The objectives are to quickly and accurately measure the transfer function relating the electric power output to the AVR PSS reference voltage input of a system with the plant operating under normal conditions. In addition, the excitation signal used in the identification procedure is such that it will not adversely affect the terminal voltage or the system frequency. This research emphasized the development of a neural network that is easily trained and robust to changing system conditions. Performance studies of the trained neural network are described. Simulation studies suggest the practical feasibility of the algorithm as a stand-alone identification package and as a portion of a self-tuning algorithm requiring identification in the strategy. The same technique applied to a forward modelling scheme can be used to test the damping contribution from different control strategies.

[1]  Mohamed A. El-Sharkawi,et al.  Identification and control of a DC motor using back-propagation neural networks , 1991 .

[2]  W. J. Wilson,et al.  Synchronous machine parameter identification: a time domain approach , 1988 .

[3]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  B. Widrow,et al.  Adaptive inverse control , 1987, Proceedings of 8th IEEE International Symposium on Intelligent Control.

[6]  Mohamed A. El-Sharkawi,et al.  High performance drive of DC brushless motors using neural network , 1994 .

[7]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1971 .

[8]  K. E. Bollinger,et al.  Time series identification of interarea and local generator resonant modes , 1995 .

[9]  Om P. Malik,et al.  An artificial neural network based adaptive power system stabilizer , 1993 .

[10]  Mohamed A. El-Sharkawi,et al.  Laboratory implementation of a neural network trajectory controller for a DC motor , 1993 .

[11]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[12]  H. Saunders.  Literature Review : RANDOM DATA: ANALYSIS AND MEASUREMENT PROCEDURES J. S. Bendat and A.G. Piersol Wiley-Interscience, New York, N. Y. (1971) , 1974 .

[13]  K. E. Bollinger,et al.  A Method for on-Line Identification of Power System Model Parameters in the Presence of Noise , 1982 .

[14]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[15]  William Eric. Norum.  Multimode damping of synchronous generator transients using self-tuning control , 1991 .