An efficient Hopfield network to solve economic dispatch problems with transmission system representation

An efficient Hopfield network to solve economic dispatch problems with transmission system representation

Autor Silva, I. N. da Google Scholar
Nepomuceno, L. Google Scholar
Bastos, T. M. Google Scholar
Instituição Universidade Federal de São Paulo (UNIFESP)
Resumo Economic dispatch (ED) problems have recently been solved by artificial neural network approaches. Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. the ability of neural networks to realize some complex non-linear function makes them attractive for system optimization. All ED models solved by neural approaches described in the literature fail to represent the transmission system. Therefore, such procedures may calculate dispatch policies, which do not take into account important active power constraints. Another drawback pointed out in the literature is that some of the neural approaches fail to converge efficiently toward feasible equilibrium points. A modified Hopfield approach designed to solve ED problems with transmission system representation is presented in this paper. the transmission system is represented through linear load flow equations and constraints on active power flows. the internal parameters of such modified Hopfield networks are computed using the valid-subspace technique. These parameters guarantee the network convergence to feasible equilibrium points, which represent the solution for the ED problem. Simulation results and a sensitivity analysis involving IEEE 14-bus test system are presented to illustrate efficiency of the proposed approach. (C) 2004 Elsevier B.V. All rights reserved.
Assunto economic dispatch
artificial neural networks
Hopfield model
Idioma Inglês
Data 2004-11-01
Publicado em International Journal of Electrical Power & Energy Systems. Oxford: Elsevier B.V., v. 26, n. 9, p. 733-738, 2004.
ISSN 0142-0615 (Sherpa/Romeo, fator de impacto)
Editor Elsevier B.V.
Extensão 733-738
Direito de acesso Acesso restrito
Tipo Artigo
Web of Science WOS:000223581600009

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