RMP model based optimization of power system stabilizers in multi-machine power system

Seung Mook Baek, Jung Wook Park

Research output: Contribution to journalArticlepeer-review


This paper describes the nonlinear parameter optimization of power system stabilizer (PSS) by using the reduced multivariate polynomial (RMP) algorithm with the one-shot property. The RMP model estimates the second-order partial derivatives of the Hessian matrix after identifying the trajectory sensitivities, which can be computed from the hybrid system modeling with a set of differential-algebraic-impulsive-switched (DAIS) structure for a power system. Then, any nonlinear controller in the power system can be optimized by achieving a desired performance measure, mathematically represented by an objective function (OF). In this paper, the output saturation limiter of the PSS, which is used to improve low-frequency oscillation damping performance during a large disturbance, is optimally tuned exploiting the Hessian estimated by the RMP model. Its performances are evaluated with several case studies on both single-machine infinite bus (SMIB) and multi-machine power system (MMPS) by time-domain simulation. In particular, all nonlinear parameters of multiple PSSs on IEEE benchmark two-area four-machine power system are optimized to be robust against various disturbances by using the weighted sum of the OFs.

Original languageEnglish
Pages (from-to)842-850
Number of pages9
JournalNeural Networks
Issue number5-6
Publication statusPublished - 2009 Jul

Bibliographical note

Funding Information:
This work was supported by Manpower Development Program for Energy & Resources of MKE with Yonsei Electric Power Research Center (YEPRC) at Yonsei University, Seoul, Republic of Korea.

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence


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