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Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems]

Abstract

The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).
Authors:
Djukanovic, M; [1]  Sobajic, D J; Pao, Yohhan [2] 
  1. Institut Nikola Tesla, Belgrade (Yugoslavia)
  2. Case Western Reserve Univ., Cleveland, OH (United States)
Publication Date:
Oct 01, 1991
Product Type:
Journal Article
Reference Number:
GB-92-053028; EDB-93-001736
Resource Relation:
Journal Name: International Journal of Electrical Power and Energy Systems; (United Kingdom); Journal Volume: 13:5
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; POWER SYSTEMS; COMPUTERIZED SIMULATION; INSTABILITY; MATHEMATICAL MODELS; NEURAL NETWORKS; PATTERN RECOGNITION; SYSTEM FAILURE ANALYSIS; TRANSIENTS; SIMULATION; SYSTEMS ANALYSIS; 240100* - Power Systems- (1990-)
OSTI ID:
7186835
Country of Origin:
United Kingdom
Language:
English
Other Identifying Numbers:
Journal ID: ISSN 0142-0615; CODEN: IEPSDC
Submitting Site:
GB
Size:
Pages: 255-262
Announcement Date:
Jan 01, 1993

Citation Formats

Djukanovic, M, Sobajic, D J, and Pao, Yohhan. Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems]. United Kingdom: N. p., 1991. Web. doi:10.1016/0142-0615(91)90048-Z.
Djukanovic, M, Sobajic, D J, & Pao, Yohhan. Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems]. United Kingdom. doi:10.1016/0142-0615(91)90048-Z.
Djukanovic, M, Sobajic, D J, and Pao, Yohhan. 1991. "Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems]." United Kingdom. doi:10.1016/0142-0615(91)90048-Z. https://www.osti.gov/servlets/purl/10.1016/0142-0615(91)90048-Z.
@misc{etde_7186835,
title = {Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems]}
author = {Djukanovic, M, Sobajic, D J, and Pao, Yohhan}
abstractNote = {The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).}
doi = {10.1016/0142-0615(91)90048-Z}
journal = {International Journal of Electrical Power and Energy Systems; (United Kingdom)}
volume = {13:5}
journal type = {AC}
place = {United Kingdom}
year = {1991}
month = {Oct}
}