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Title: Neural network-based self-organizing fuzzy controller for transient stability of multimachine power systems

Abstract

An efficient self-organizing neural fuzzy controller (SONFC) is designed to improve the transient stability of multimachine power systems. First, an artificial neural network (ANN)-based model is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the ANN model. With the excellent learning capability inherent in the ANN, the traditional heuristic fuzzy control rules and input-output fuzzy membership functions can be optimally tuned from training examples by the back propagation learning algorithm. Considerable rule-matching times of the inference engine in the traditional fuzzy system can be saved. To illustrate the performance and usefulness of the SONFC, comparative studies with a bang-bang controller are performed on the 34-generator Taipower system with rather encouraging results.

Authors:
;  [1]
  1. National Taiwan Institute of Technology, Taipei (Taiwan, Province of China). Dept. of Electrical Engineering
Publication Date:
OSTI Identifier:
94102
Report Number(s):
CONF-940702-
Journal ID: ITCNE4; ISSN 0885-8969; TRN: IM9538%%75
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Energy Conversion
Additional Journal Information:
Journal Volume: 10; Journal Issue: 2; Conference: 1994 Institute of Electrical and Electronic Engineers/Power Engineering Society (IEEE/PES) summer meeting, San Francisco, CA (United States), 24-28 Jul 1994; Other Information: PBD: Jun 1995
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; POWER SYSTEMS; CONTROL SYSTEMS; STABILITY; ELECTRICAL TRANSIENTS; FUZZY LOGIC

Citation Formats

Chang, H C, and Wang, M H. Neural network-based self-organizing fuzzy controller for transient stability of multimachine power systems. United States: N. p., 1995. Web. doi:10.1109/60.391901.
Chang, H C, & Wang, M H. Neural network-based self-organizing fuzzy controller for transient stability of multimachine power systems. United States. https://doi.org/10.1109/60.391901
Chang, H C, and Wang, M H. 1995. "Neural network-based self-organizing fuzzy controller for transient stability of multimachine power systems". United States. https://doi.org/10.1109/60.391901.
@article{osti_94102,
title = {Neural network-based self-organizing fuzzy controller for transient stability of multimachine power systems},
author = {Chang, H C and Wang, M H},
abstractNote = {An efficient self-organizing neural fuzzy controller (SONFC) is designed to improve the transient stability of multimachine power systems. First, an artificial neural network (ANN)-based model is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the ANN model. With the excellent learning capability inherent in the ANN, the traditional heuristic fuzzy control rules and input-output fuzzy membership functions can be optimally tuned from training examples by the back propagation learning algorithm. Considerable rule-matching times of the inference engine in the traditional fuzzy system can be saved. To illustrate the performance and usefulness of the SONFC, comparative studies with a bang-bang controller are performed on the 34-generator Taipower system with rather encouraging results.},
doi = {10.1109/60.391901},
url = {https://www.osti.gov/biblio/94102}, journal = {IEEE Transactions on Energy Conversion},
number = 2,
volume = 10,
place = {United States},
year = {Thu Jun 01 00:00:00 EDT 1995},
month = {Thu Jun 01 00:00:00 EDT 1995}
}