Nonlinear controller optimization of a power system based on reduced multivariate polynomial model

Authors:
Seung-Mook Baek;Jung-Wook Park
Affiliations:
School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea;School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 4
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Abstract

This paper describes the design of a nonlinear controller in a power system by using the reduced multivariate polynomial (RMP) optimization algorithm with the one-shot training property. The RMP model is applied to estimate its Hessian matrix in addition to identifying the trajectory sensitivities obtained from hybrid system modeling for the power system. In this paper, the saturation limiter of the power system stabilizer (PSS), which is an important nonlinear controller to improve low-frequency oscillation damping performance, is tuned optimally by using Hessian matrix estimated by the RMP model. The performance of the optimal output limits determined by the proposed method is evaluated by applying the large disturbance such as a three-phase short circuit to a power system.