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

Seung Mook Baek; Jung Wook Park

doi:10.1109/INDIN.2008.4618233

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

Seung Mook Baek, Jung Wook Park

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings - IEEE INDIN 2008
Subtitle of host publication	6th IEEE International Conference on Industrial Informatics
Pages	921-926
Number of pages	6
DOIs	https://doi.org/10.1109/INDIN.2008.4618233
Publication status	Published - 2008
Event	IEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics - Daejeon, Korea, Republic of Duration: 2008 Jul 13 → 2008 Jul 16

Publication series

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

Other

Other	IEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics
Country/Territory	Korea, Republic of
City	Daejeon
Period	08/7/13 → 08/7/16

Bibliographical note

Funding Information:
The support of this effort through the Petroleum Institute and the Center for Environmental Energy Engineering (CEEE) at the University of Maryland is gratefully acknowledged.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Information Systems

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10.1109/INDIN.2008.4618233

Cite this

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title = "Nonlinear parameter neuro-estimation for optimal tuning of power system stabilizers",

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).",

author = "Baek, {Seung Mook} and Park, {Jung Wook}",

note = "Funding Information: The support of this effort through the Petroleum Institute and the Center for Environmental Energy Engineering (CEEE) at the University of Maryland is gratefully acknowledged.; IEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics ; Conference date: 13-07-2008 Through 16-07-2008",

year = "2008",

doi = "10.1109/INDIN.2008.4618233",

language = "English",

isbn = "9781424421718",

series = "IEEE International Conference on Industrial Informatics (INDIN)",

pages = "921--926",

booktitle = "Proceedings - IEEE INDIN 2008",

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Baek, SM & Park, JW 2008, Nonlinear parameter neuro-estimation for optimal tuning of power system stabilizers. in Proceedings - IEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics., 4618233, IEEE International Conference on Industrial Informatics (INDIN), pp. 921-926, IEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics, Daejeon, Korea, Republic of, 08/7/13. https://doi.org/10.1109/INDIN.2008.4618233

Nonlinear parameter neuro-estimation for optimal tuning of power system stabilizers. / Baek, Seung Mook; Park, Jung Wook.
Proceedings - IEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics. 2008. p. 921-926 4618233 (IEEE International Conference on Industrial Informatics (INDIN)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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).

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

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