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Title: Synchronous machine steady-state stability analysis using an artificial neural network

Abstract

A new type of artificial neural network is proposed for the steady-state stability analysis of a synchronous generator. In the developed artificial neutral network, those system variables which play an important role in steady-state stability such as generator outputs and power system stabilizer parameters are employed as the inputs. The output of the neural net provides the information on steady-state stability. Once the connection weights of the neural network have been learned using a set of training data derived off-line, the neural net can be applied to analyze the steady-state stability of the system time. To demonstrate the effectiveness of the proposed neural net, steady-state stability analysis is performed on a synchronous generator connected to a large power system. It is found that the proposed neural net requires much less training time than the multilayer feedforward network with backpropagation-momentum learning algorithm. It is also concluded from the test results that correct stability assessment can be achieved by the neural network.

Authors:
;  [1]
  1. National Taiwan Univ., Taipei (Taiwan). Dept. of Electrical Engineering
Publication Date:
OSTI Identifier:
5793812
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (USA)
Additional Journal Information:
Journal Volume: 6:1; Journal ID: ISSN 0885-8969
Country of Publication:
United States
Language:
English
Subject:
20 FOSSIL-FUELED POWER PLANTS; 24 POWER TRANSMISSION AND DISTRIBUTION; ELECTRIC GENERATORS; COMPUTER CALCULATIONS; ALGORITHMS; ARTIFICIAL INTELLIGENCE; OPERATION; POWER SYSTEMS; STABILITY; STEADY-STATE CONDITIONS; ENERGY SYSTEMS; MATHEMATICAL LOGIC; 200104* - Fossil-Fueled Power Plants- Components; 240100 - Power Systems- (1990-)

Citation Formats

Chen, C R, and Hsu, Y Y. Synchronous machine steady-state stability analysis using an artificial neural network. United States: N. p., 1991. Web. doi:10.1109/60.73784.
Chen, C R, & Hsu, Y Y. Synchronous machine steady-state stability analysis using an artificial neural network. United States. https://doi.org/10.1109/60.73784
Chen, C R, and Hsu, Y Y. 1991. "Synchronous machine steady-state stability analysis using an artificial neural network". United States. https://doi.org/10.1109/60.73784.
@article{osti_5793812,
title = {Synchronous machine steady-state stability analysis using an artificial neural network},
author = {Chen, C R and Hsu, Y Y},
abstractNote = {A new type of artificial neural network is proposed for the steady-state stability analysis of a synchronous generator. In the developed artificial neutral network, those system variables which play an important role in steady-state stability such as generator outputs and power system stabilizer parameters are employed as the inputs. The output of the neural net provides the information on steady-state stability. Once the connection weights of the neural network have been learned using a set of training data derived off-line, the neural net can be applied to analyze the steady-state stability of the system time. To demonstrate the effectiveness of the proposed neural net, steady-state stability analysis is performed on a synchronous generator connected to a large power system. It is found that the proposed neural net requires much less training time than the multilayer feedforward network with backpropagation-momentum learning algorithm. It is also concluded from the test results that correct stability assessment can be achieved by the neural network.},
doi = {10.1109/60.73784},
url = {https://www.osti.gov/biblio/5793812}, journal = {IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers); (USA)},
issn = {0885-8969},
number = ,
volume = 6:1,
place = {United States},
year = {Fri Mar 01 00:00:00 EST 1991},
month = {Fri Mar 01 00:00:00 EST 1991}
}