Abstract
A practical parameter estimation procedure for a real excitation system is reported in this paper. The core algorithm is based on genetic algorithm (GA) which estimates the parameters of a real AC brushless excitation system with limited information about the system. Practical considerations are integrated in the estimation procedure to reduce the complexity of the problem. The effectiveness of the proposed technique is demonstrated via real measurements. Besides, it is seen that GA can converge to a satisfactory solution even when starting from large initial variation ranges of the estimated parameters. The whole methodology is described and the estimation strategy is presented in this paper.
G. Yang and J. Østergaard is with Centre for Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, DK. L. Zeni is with Department of Wind Energy, Technical University of Denmark, 4000 Roskilde, DK. G. C. Tarnowski is with Vestas Wind Systems and Centre for Electric Technology, Department of Electrical Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, DK.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kundur, P., Balu, N., Lauby, M.: Power System Stability and Control. McGraw-Hill Professional (1994)
Ferguson, R., Herbst, H., Miller, R.: Analytical studies of the brushless excitation system. AIEE Transactions on Power Apparatus and Systems Part III 79, 1815–1821 (1959)
Stigers, C., Hurley, J., Gorden, D., Callanan, D.: Field tests and simulation of a high initial response brushless excitation system. IEEE Transactions on Energy Conversion 1(1), 2–10 (1986)
Witzke, R., Kresser, J., Dillard, J.: Influence of AC reactance on voltage regulation of six-phase rectifiers. AIEE Transactions 72, 244–253 (1953)
Lee, D.: IEEE recommended practice for excitation system models for power system stability studies (IEEE Std. 421.5–1992). Energy Development and Power Generation Committee of the IEEE Power Engineering Society (1992)
Sanchez-Gasca, J., Bridenbaugh, C., Bowler, C., Edmonds, J.: Trajectory sensitivity based identification of synchronous generator and excitation system parameters. IEEE Transactions on Power Systems 3(4), 1814–1822 (1988)
Bujanowski, B., Pierre, J., Hietpas, S., Sharpe, T., Pierre, D.: A comparison of several system identification methods with application to power systems. In: Proceedings of the 36th Midwest Symposium on Circuits and Systems, pp. 64–67. IEEE (1993)
Liu, C., Hsu, Y., Jeng, L., Lin, C., Huang, C., Liu, A., Li, T.: Identification of exciter constants using a coherence function based weighted least squares approach. IEEE Transactions on Energy Conversion 8(3), 460–467 (1993)
Shande, S., Shouzhen, Z., Bo, H.: Identification of parameters of synchronous machine and excitation system by online test. In: International Conference on Advances in Power System Control, Operation and Management, pp. 716–719. IET (2002)
Feng, S., Jianbo, X., Guoping, W., Yong-hong, X.: Study of brushless excitation system parameters estimation based on improved genetic algorithm. In: 3rd International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, pp. 915–919 (2008)
Puma, J., Colomé, D.: Parameters identification of excitation system models using genetic algorithms. IET Generation, Transmission & Distribution 2(3), 456–467 (2008)
Sivanandam, S., Deepa, S.: Introduction to genetic algorithms. Springer (2007)
Goldberg, D.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley (1989)
Conn, A., Gould, N., Toint, P.: A globally convergent augmented Lagrangian barrier algorithm for optimization with general inequality constraints and simple bounds. Mathematics of Computation 66(217), 261–288 (1997)
Krause, P., Wasynczuk, O., Sudhoff, S.: Analysis of electric machinery and drive systems. IEEE Press (2002)
DIgSILENT, “Synchronous generator technical documentation,” DIgSILENT PowerFactory, Tech. Rep. (2007)
Andriollo, M., Martinelli, G., Morini, A.: Macchine elettriche rotanti. Ed. Libreria Cortina, Padova (2003)
Ogata, K.: Modern control engineering, 4th edn. Prentice Hall (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zeni, L., Yang, G., Tarnowski, G.C., Østergaard, J. (2014). A Practical Approach for Parameter Identification with Limited Information. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8482. Springer, Cham. https://doi.org/10.1007/978-3-319-07467-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-07467-2_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07466-5
Online ISBN: 978-3-319-07467-2
eBook Packages: Computer ScienceComputer Science (R0)