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

Seung Mook Baek, Jung Wook Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages221-228
Number of pages8
DOIs
Publication statusPublished - 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: 2009 Jun 142009 Jun 19

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2009 International Joint Conference on Neural Networks, IJCNN 2009
CountryUnited States
CityAtlanta, GA
Period09/6/1409/6/19

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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  • Cite this

    Baek, S. M., & Park, J. W. (2009). Nonlinear controller optimization of a power system based on reduced multivariate polynomial model. In 2009 International Joint Conference on Neural Networks, IJCNN 2009 (pp. 221-228). [5178604] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2009.5178604