Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/54913
Type: Artigo de periódico
Title: Artificial neural networks for load flow and external equivalents studies
Author: Muller, HH
Rider, MJ
Castro, CA
Abstract: In this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits. An analysis of the input data required as well as the ANN architecture is presented. A multilayer perceptron trained with the Levenberg-Marquardt second order method is used. The proposed methodology was tested with the IEEE 30- and 57-bus, and an ill-conditioned 11-bus system. Normal operating conditions (base case) and several contingency situations including different load and generation scenarios have been considered. Simulation results show the excellent performance of the ANN for solving problems (a)-(d). (C) 2010 Elsevier B.V. All rights reserved.
Subject: Artificial neural networks
Load flow
Reactive power limits of generation buses
Load flow with step size optimization
Static external equivalents
Country: Suíça
Editor: Elsevier Science Sa
Rights: fechado
Identifier DOI: 10.1016/j.epsr.2010.01.008
Date Issue: 2010
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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