Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost function
Autor(a) principal: | |
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Data de Publicação: | 2002 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IJCNN.2002.1007658 http://hdl.handle.net/11449/33348 |
Resumo: | Economic Dispatch (ED) problems have recently been solved by artificial neural networks approaches. In most of these dispatch models, the cost function must be linear or quadratic. Therefore, functions that have several minimum points represent a problem to the simulation since these approaches have not accepted nonlinear cost function. Another drawback pointed out in the literature is that some of these neural approaches fail to converge efficiently towards feasible equilibrium points. This paper discusses the application of a modified Hopfield architecture for solving ED problems defined by nonlinear cost function. The internal parameters of the neural network adopted here are computed using the valid-subspace technique, which guarantees convergence to equilibrium points that represent a solution for the ED problem. Simulation results and a comparative analysis involving a 3-bus test system are presented to illustrate efficiency of the proposed approach. |
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Repositório Institucional da UNESP |
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spelling |
Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost functionEconomic Dispatch (ED) problems have recently been solved by artificial neural networks approaches. In most of these dispatch models, the cost function must be linear or quadratic. Therefore, functions that have several minimum points represent a problem to the simulation since these approaches have not accepted nonlinear cost function. Another drawback pointed out in the literature is that some of these neural approaches fail to converge efficiently towards feasible equilibrium points. This paper discusses the application of a modified Hopfield architecture for solving ED problems defined by nonlinear cost function. The internal parameters of the neural network adopted here are computed using the valid-subspace technique, which guarantees convergence to equilibrium points that represent a solution for the ED problem. Simulation results and a comparative analysis involving a 3-bus test system are presented to illustrate efficiency of the proposed approach.UNESP, State Univ São Paulo, FE, DEE, BR-17033360 Bauru, SP, BrazilUNESP, State Univ São Paulo, FE, DEE, BR-17033360 Bauru, SP, BrazilInstitute of Electrical and Electronics Engineers (IEEE)Universidade Estadual Paulista (Unesp)da Silva, I. N.Nepomuceno, L.Bastos, T. M.2014-05-20T15:22:21Z2014-05-20T15:22:21Z2002-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1160-1165http://dx.doi.org/10.1109/IJCNN.2002.1007658Proceeding of the 2002 International Joint Conference on Neural Networks, Vols 1-3. New York: IEEE, p. 1160-1165, 2002.1098-7576http://hdl.handle.net/11449/3334810.1109/IJCNN.2002.1007658WOS:0001774028002072013445187247691Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceeding of the 2002 International Joint Conference on Neural Networks, Vols 1-3info:eu-repo/semantics/openAccess2021-10-23T21:41:34Zoai:repositorio.unesp.br:11449/33348Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:41:34Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost function |
title |
Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost function |
spellingShingle |
Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost function da Silva, I. N. |
title_short |
Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost function |
title_full |
Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost function |
title_fullStr |
Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost function |
title_full_unstemmed |
Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost function |
title_sort |
Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost function |
author |
da Silva, I. N. |
author_facet |
da Silva, I. N. Nepomuceno, L. Bastos, T. M. |
author_role |
author |
author2 |
Nepomuceno, L. Bastos, T. M. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
da Silva, I. N. Nepomuceno, L. Bastos, T. M. |
description |
Economic Dispatch (ED) problems have recently been solved by artificial neural networks approaches. In most of these dispatch models, the cost function must be linear or quadratic. Therefore, functions that have several minimum points represent a problem to the simulation since these approaches have not accepted nonlinear cost function. Another drawback pointed out in the literature is that some of these neural approaches fail to converge efficiently towards feasible equilibrium points. This paper discusses the application of a modified Hopfield architecture for solving ED problems defined by nonlinear cost function. The internal parameters of the neural network adopted here are computed using the valid-subspace technique, which guarantees convergence to equilibrium points that represent a solution for the ED problem. Simulation results and a comparative analysis involving a 3-bus test system are presented to illustrate efficiency of the proposed approach. |
publishDate |
2002 |
dc.date.none.fl_str_mv |
2002-01-01 2014-05-20T15:22:21Z 2014-05-20T15:22:21Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/IJCNN.2002.1007658 Proceeding of the 2002 International Joint Conference on Neural Networks, Vols 1-3. New York: IEEE, p. 1160-1165, 2002. 1098-7576 http://hdl.handle.net/11449/33348 10.1109/IJCNN.2002.1007658 WOS:000177402800207 2013445187247691 |
url |
http://dx.doi.org/10.1109/IJCNN.2002.1007658 http://hdl.handle.net/11449/33348 |
identifier_str_mv |
Proceeding of the 2002 International Joint Conference on Neural Networks, Vols 1-3. New York: IEEE, p. 1160-1165, 2002. 1098-7576 10.1109/IJCNN.2002.1007658 WOS:000177402800207 2013445187247691 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceeding of the 2002 International Joint Conference on Neural Networks, Vols 1-3 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1160-1165 |
dc.publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers (IEEE) |
publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers (IEEE) |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799965201693409280 |