Artificial neural networks for load flow and external equivalents studies
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Coadvisor
Graduate program
Undergraduate course
Journal Title
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Volume Title
Publisher
Elsevier B.V. Sa
Type
Article
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Acesso restrito
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.
Description
Keywords
Artificial neural networks, Load flow, Reactive power limits of generation buses, Load flow with step size optimization, Static external equivalents
Language
English
Citation
Electric Power Systems Research. Lausanne: Elsevier B.V. Sa, v. 80, n. 9, p. 1033-1041, 2010.
