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Title: A neural network-based power system stabilizer using power flow characteristics

Abstract

A neural network-based Power System Stabilizer (Neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The uses of power flow dynamics provide a PSS for a wide range operation with reduced size neutral networks. The Neuro-PSS consists of two neutral networks: Neuro-Identifier and Neuro-Controller. The low-frequency oscillation is modeled by the Neuro-Identifier using the power flow dynamics, then a Generalized Backpropagation-Thorough-Time (GBTT) algorithm is developed to train the Neuro-Controller. The simulation results show that the Neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS.

Authors:
;  [1];  [2]
  1. Seoul National Univ. (Korea, Republic of). Dept. of Electrical Engineering
  2. Pennsylvania State Univ., University Park, PA (United States). Dept. of Electrical Engineering
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
276831
Report Number(s):
CONF-960111-
Journal ID: ITCNE4; ISSN 0885-8969; TRN: IM9636%%506
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Energy Conversion
Additional Journal Information:
Journal Volume: 11; Journal Issue: 2; Conference: IEEE Power Engineering Society (PES) Winter meeting, Baltimore, MD (United States), 21-25 Jan 1996; Other Information: PBD: Jun 1996
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; POWER SYSTEMS; STABILIZATION; CONTROL SYSTEMS; NEURAL NETWORKS; PERFORMANCE; DESIGN; MATHEMATICAL MODELS; FREQUENCY DEPENDENCE; OSCILLATIONS

Citation Formats

Park, Y M, Choi, M S, and Lee, K Y. A neural network-based power system stabilizer using power flow characteristics. United States: N. p., 1996. Web. doi:10.1109/60.507657.
Park, Y M, Choi, M S, & Lee, K Y. A neural network-based power system stabilizer using power flow characteristics. United States. https://doi.org/10.1109/60.507657
Park, Y M, Choi, M S, and Lee, K Y. 1996. "A neural network-based power system stabilizer using power flow characteristics". United States. https://doi.org/10.1109/60.507657.
@article{osti_276831,
title = {A neural network-based power system stabilizer using power flow characteristics},
author = {Park, Y M and Choi, M S and Lee, K Y},
abstractNote = {A neural network-based Power System Stabilizer (Neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The uses of power flow dynamics provide a PSS for a wide range operation with reduced size neutral networks. The Neuro-PSS consists of two neutral networks: Neuro-Identifier and Neuro-Controller. The low-frequency oscillation is modeled by the Neuro-Identifier using the power flow dynamics, then a Generalized Backpropagation-Thorough-Time (GBTT) algorithm is developed to train the Neuro-Controller. The simulation results show that the Neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS.},
doi = {10.1109/60.507657},
url = {https://www.osti.gov/biblio/276831}, journal = {IEEE Transactions on Energy Conversion},
number = 2,
volume = 11,
place = {United States},
year = {Sat Jun 01 00:00:00 EDT 1996},
month = {Sat Jun 01 00:00:00 EDT 1996}
}