Identification of synchronous generators using adaptive wavelet networks

Application of wavelet networks for the identification of a synchronous generator is described in this paper. Parameter adaptation laws are used to track the variations in the parameters, following changes in the generator operating conditions. The adaptation laws have been developed using a Lyapunov function. This guarantees the stability of the identification algorithm and also ensures the convergence of parameters and variables. The proposed method has been tested on a synchronous machine. Experimental results show good accuracy of the identified model and robustness of the algorithm following severe changes in the operating conditions.

[1]  Stephen A. Billings,et al.  Identi cation of nonlinear systems-A survey , 1980 .

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

[3]  Ali Keyhani,et al.  An algebraic approach for identifying operating point dependent parameters of synchronous machines using orthogonal series expansions , 2001 .

[4]  Y. H. Ku,et al.  Electric Power System Dynamics , 1983 .

[5]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[6]  O. P. Malik,et al.  On-line identification of synchronous generator using neural networks , 1996, Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering.

[7]  Daniel W. C. Ho,et al.  Adaptive wavelet networks for nonlinear system identification , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[8]  Jay A. Farrell,et al.  Wavelet-based system identification for nonlinear control , 1999, IEEE Trans. Autom. Control..

[9]  Guoping Liu,et al.  Nonlinear system identification using wavelet networks , 2000, Int. J. Syst. Sci..

[10]  O. Nelles.  Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models , 2000 .

[11]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[12]  L. A. Kilgore,et al.  Calculation of Synchronous Machine Constants- Reactances and Time Constants Affecting Transient Characteristics , 1931, Transactions of the American Institute of Electrical Engineers.

[13]  Loi Lei Lai,et al.  Application of evolutionary programming to transient and subtransient parameter estimation , 1996 .

[14]  B. Torrésani,et al.  Wavelets: Mathematics and Applications , 1994 .

[15]  Leonardo Maria Reyneri.  Unification of neural and wavelet networks and fuzzy systems , 1999, IEEE Trans. Neural Networks.

[16]  Stephen A. Billings,et al.  Non-linear system identification using neural networks , 1990 .

[17]  Ali Keyhani,et al.  Synchronous machine parameter estimation using the Hartley series , 2001 .