Abstract
This paper presents a power system stabilizer design that uses a generalized minimum variance-inverse dynamic neuro controller, which is the combination of the inverse dynamic neural model, the generalized minimum variance, and the neuro compensator. An inverse dynamic neural model represents the inverse dynamics of the system. The inverse dynamic neural model is trained to provide control input into the system, which makes the plant output reach the target value at the next sampling time. Once the inverse dynamic neural model is trained, it does not require retuning for cases with other types of disturbances. In this paper, a generalized minimum variance control scheme is adapted to prevent unstable system performance caused by non-minimum phase characteristics. In addition, a neural compensator is designed to compensate for modeling errors. The proposed control scheme is tested in a multimachine power system.
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
de Mello, F.P., Concordia, C.A.: Concept of Synchronous Machine Stability as Affected by Excitation Control. IEEE Trans. on Power Apparatus and Systems 88(4), 316–329 (1969)
de Mello, F.P., Nolan, P.J., Laskowski, T.F., Undrill, J.M.: Coordinate Application of Stabilizers in Multimachine Power System. IEEE Trans. on Power Apparatus and Systems 99, 892–901 (1980)
Doi, S.A.: Coordinated Synthesis of Power System Stabilizers in Multimachine Power Systems. IEEE Trans. on Power Apparatus and Systems 103, 1473–1479 (1984)
Hwang, T.L., Hwang, T.Y., Yang, W.T.: Two-level Optimal Output Feedback Stabilizer Design. IEEE Trans. on Power Systems 6(3), 1042–1048 (1991)
Ghosh, A., Ledwich, G., Malik, O.P., Hoper, G.S.: Power System Stabilizer Based on Adaptive Control Techniques. IEEE Trans. on Power Apparatus and Systems 103, 1983–1989 (1984)
Pierre, D.A.: A Perspective on Adaptive Control of Power Systems. IEEE Trans. on Power Systems 2, 387–396 (1987)
Zhang, Y., Malik, O.P., Hope, G.S., Chen, G.P.: Application of an Inverse Input/Output Mapped ANN as a Power System Stabilizer. IEEE Trans. on Energy Conversion 9(3), 433–441 (1994)
Hsu, Y.Y., Chen, C.R.: Tuning of Power System Stabilizers Using an ANN. IEEE Trans. on Energy Conversion 6(4), 612–619 (1991)
Wu, Q.H., Hogg, B.W., Irwin, G.W.: A Neural Network Regulator for Turbogenerators. IEEE Trans. on Neural Networks 3(1), 95–100 (1992)
Ko, H.-S., Lee, K.Y., Kim, H.-C.: An Intelligent Based LQR Controller Design to Power System Stabilization. Electrical Power System Research 71(1), 1–9 (2004)
Astrom, K., Wittenmark, B.: Adaptive Control. Addison Wesley, Reading (1995)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, New Jersey (1999)
Burden, R.L., Faires, J.D.: Numerical Analysis, PWS-KENT (1989)
Sauer, P.W., Pai, M.A.: Power System Dynamics and Stability. Prentice Hall, New Jersey (1998)
Abido, M.A.: Particle Swarm Optimization for Multimachine Power System Stabilizer Design. In: IEEE Power Engineering Society Summer Meeting, vol. 3, pp. 1346–1351 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ko, HS., Lee, K.Y., Kang, MJ., Kim, HC. (2006). Generalized Minimum Variance Neuro Controller for Power System Stabilization. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_194
Download citation
DOI: https://doi.org/10.1007/11760023_194
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)