Nonlinear parameter neuro-estimation for optimal tuning of power system stabilizers

Seung Mook Baek, Jung Wook Park

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

Abstract

This paper describes nonlinear parameter estimation of non-smooth nonlinear device by using a feed-forward neural network (FFNN) embedded in a hybrid system modeling. The hybrid systems are modeled by the differential-algebraic- impulsive-switched (DAIS) structure. In a switched linear hybrid system, the FFNN is applied to identify full dynamics of an objective function J formed by the states. Moreover, the partial derivatives of function J with respect to the each state are approximated by the computation of the backpropagation through the FFNN. Then, this paper focuses on the FFNN based estimator for the non-smooth nonlinear dynamic behaviors due to saturation limiter of the power system stabilizer (PSS) in both a single machine infinite bus (SMIB) system and a multi-machine power system (MMPS).

Original languageEnglish
Title of host publicationProceedings - IEEE INDIN 2008
Subtitle of host publication6th IEEE International Conference on Industrial Informatics
Pages921-926
Number of pages6
DOIs
Publication statusPublished - 2008
EventIEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics - Daejeon, Korea, Republic of
Duration: 2008 Jul 132008 Jul 16

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
ISSN (Print)1935-4576

Other

OtherIEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics
CountryKorea, Republic of
CityDaejeon
Period08/7/1308/7/16

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems

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