Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network

Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network

Author Ferreira, W. P. Google Scholar
Silveira, MDG Google Scholar
Lotufo, ADP Google Scholar
Minussi, C. R. Google Scholar
Institution Universidade Federal de São Paulo (UNIFESP)
Abstract This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural network for real time applications. the security margin is used as a stability analysis criterion, considering three-phase short circuit faults with a transmission line outage. the neural network operation consists of two fundamental phases: the training and the analysis. the training phase needs a great quantity of processing for the realization, while the analysis phase is effectuated almost without computation effort. This is, therefore the principal purpose to use neural networks for solving complex problems that need fast solutions, as the applications in real time. the ART neural networks have as primordial characteristics the plasticity and the stability, which are essential qualities to the training execution and to an efficient analysis. the fuzzy ART-ARTMAP neural network is proposed seeking a superior performance, in terms of precision and speed, when compared to conventional ARTMAP, and much more when compared to the neural networks that use the training by backpropagation algorithm, which is a benchmark in neural network area. (c) 2005 Elsevier B.V. All rights reserved.
Keywords adaptive resonance theory
Language English
Date 2006-04-01
Published in Electric Power Systems Research. Lausanne: Elsevier Science Sa, v. 76, n. 6-7, p. 466-475, 2006.
ISSN 0378-7796 (Sherpa/Romeo, impact factor)
Publisher Elsevier B.V.
Extent 466-475
Access rights Closed access
Type Article
Web of Science ID WOS:000236048100009

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