Optimal tuning for linear and nonlinear parameters of power system stabilizers in hybrid system modeling

Seung Mook Baek, Jung Wook Park, Ian A. Hiskens

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

This paper focuses on the systematic optimal tuning of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. As the PSS consists of both linear parameters (such as the gain and time constant) and nonsmooth nonlinear parameters (such as saturation limits of the PSS), two methods are applied for the optimal tuning of all parameters. One is to use the optimization technique based on the Hessian matrix estimated by the feedforward neural network, which identifies the first-order derivatives obtained by the trajectory sensitivities, for the nonlinear parameters. Moreover, the other is to use the eigenvalue analysis for the linear parameters. The performances of parameters optimized by the proposed method are evaluated by the case studies based on time-domain simulation and real-time hardware implementation.

Original languageEnglish
Pages (from-to)87-97
Number of pages11
JournalIEEE Transactions on Industry Applications
Volume45
Issue number1
DOIs
Publication statusPublished - 2009 Feb 12

Fingerprint

Hybrid systems
Tuning
Feedforward neural networks
Damping
Trajectories
Derivatives
Hardware

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

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Optimal tuning for linear and nonlinear parameters of power system stabilizers in hybrid system modeling. / Baek, Seung Mook; Park, Jung Wook; Hiskens, Ian A.

In: IEEE Transactions on Industry Applications, Vol. 45, No. 1, 12.02.2009, p. 87-97.

Research output: Contribution to journalArticle

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