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Neural net based determination of generator-shedding requirements in electric power systems

Journal Article:

Abstract

This paper presents an application of artificial neural networks (ANN) in support of a decision-making process by power system operators directed towards the fast stabilisation of multi-machine systems. The proposed approach considers generator shedding as the most effective discrete supplementary control for improving the dynamic performance of faulted power systems and preventing instabilities. The sensitivity of the transient energy function (TEF) with respect to changes in the amount of dropped generation is used during the training phase of ANNs to assess the critical amount of generator shedding required to prevent the loss of synchronism. The learning capabilities of neural nets are used to establish complex mappings between fault information and the amount of generation to be shed, suggesting it as the control signal to the power system operator. (author)
Authors:
Djukanovic, M; [1]  Sobajic, D J; Pao, Y -H [2] 
  1. Electrical Engineering Inst. 'Nikola Tesla', Belgrade (Yugoslavia)
  2. Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Applied Physics Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Computer Engineering and Science AI WARE Inc., Cleveland, OH (United States)
Publication Date:
Sep 01, 1992
Product Type:
Journal Article
Reference Number:
GB-93-050214; EDB-93-089561
Resource Relation:
Journal Name: IEE Proceedings, Part C: Generation, Transmission and Distribution (Institution of Electrical Engineers); (United Kingdom); Journal Volume: 139:5
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; POWER SYSTEMS; NEURAL NETWORKS; STABILIZATION; CONTROL SYSTEMS; ELECTRICAL TRANSIENTS; TRANSIENTS; VOLTAGE DROP; 240100* - Power Systems- (1990-)
OSTI ID:
6503134
Country of Origin:
United Kingdom
Language:
English
Other Identifying Numbers:
Journal ID: ISSN 0143-7046; CODEN: IPPDDA
Submitting Site:
GB
Size:
Pages: 427-436
Announcement Date:

Journal Article:

Citation Formats

Djukanovic, M, Sobajic, D J, and Pao, Y -H. Neural net based determination of generator-shedding requirements in electric power systems. United Kingdom: N. p., 1992. Web.
Djukanovic, M, Sobajic, D J, & Pao, Y -H. Neural net based determination of generator-shedding requirements in electric power systems. United Kingdom.
Djukanovic, M, Sobajic, D J, and Pao, Y -H. 1992. "Neural net based determination of generator-shedding requirements in electric power systems." United Kingdom.
@misc{etde_6503134,
title = {Neural net based determination of generator-shedding requirements in electric power systems}
author = {Djukanovic, M, Sobajic, D J, and Pao, Y -H}
abstractNote = {This paper presents an application of artificial neural networks (ANN) in support of a decision-making process by power system operators directed towards the fast stabilisation of multi-machine systems. The proposed approach considers generator shedding as the most effective discrete supplementary control for improving the dynamic performance of faulted power systems and preventing instabilities. The sensitivity of the transient energy function (TEF) with respect to changes in the amount of dropped generation is used during the training phase of ANNs to assess the critical amount of generator shedding required to prevent the loss of synchronism. The learning capabilities of neural nets are used to establish complex mappings between fault information and the amount of generation to be shed, suggesting it as the control signal to the power system operator. (author)}
journal = {IEE Proceedings, Part C: Generation, Transmission and Distribution (Institution of Electrical Engineers); (United Kingdom)}
volume = {139:5}
journal type = {AC}
place = {United Kingdom}
year = {1992}
month = {Sep}
}