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: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper focuses on the systematic optimal tuning of a 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 non-smooth nonlinear parameters, such as saturation limits of the PSS, two methods are applied to achieve optimal tuning of all parameters. One is to use the optimization technique based on the Hessian matrix estimated by the feed-forward neural network (FFNN), which identifies the first-order derivatives obtained by the trajectory sensitivities, for the nonlinear parameters. The other is to use an eigenvalue analysis for the linear parameters. The performance of parameters optimized by the proposed method is evaluated by time-domain simulation in both a single-machine infinite bus (SMIB) system and a multi-machine power system (MMPS).

Original languageEnglish
Title of host publicationConference Record of the 2007 IEEE Industry Applications Conference 42nd Annual Meeting, IAS
Pages1665-1672
Number of pages8
DOIs
Publication statusPublished - 2007
Event2007 IEEE Industry Applications Conference 42nd Annual Meeting, IAS - New Orleans, LA, United States
Duration: 2007 Sept 232007 Sept 27

Publication series

NameConference Record - IAS Annual Meeting (IEEE Industry Applications Society)
ISSN (Print)0197-2618

Other

Other2007 IEEE Industry Applications Conference 42nd Annual Meeting, IAS
Country/TerritoryUnited States
CityNew Orleans, LA
Period07/9/2307/9/27

All Science Journal Classification (ASJC) codes

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

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