Application of fuzzy c-means clustering in power system model reduction for controller design

Authors:
Shu-Chen Wang;Pei-Hwa Huang;Chi-Jui Wu
Affiliations:
Department of Electrical Engineering, National Taiwan Ocean University, Keelung, Taiwan;Department of Electrical Engineering, National Taiwan Ocean University, Keelung, Taiwan;Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
Venue:
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Year:
2006

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Abstract

This paper presents the application of fuzzy c-means (FCM) clustering in the order reduction of dynamic models for controller design in a power system. Based on the fuzzy c-means algorithm, a method is proposed for clustering the poles and zeros of the original power system model into new clusters from which a reduced-order model can be obtained. Then the reduced-order model is used to design a proportional-integral type power system stabilizer to improve the damping in system oscillation after a system disturbance. The reduced-order model can contain the critical dynamic characteristics of the original model, but let it easier to design the controller. Results from a sample power system are presented to show the validity of the proposed method. The electromechanical mode of the power system can be improved by the designed power system stabilier from pole assignment.