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://hdl.handle.net/11449/224238 |
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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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.State University of Sao Paulo-UNESP UNESP/FE/DEE, CP 473, CEP 17033-360 Bauru - SPState University of Sao Paulo-UNESP UNESP/FE/DEE, CP 473, CEP 17033-360 Bauru - SPUniversidade Estadual Paulista (UNESP)Nunes da Silva, Ivan [UNESP]Nepomuceno, Leonardo [UNESP]Bastos, Thiago Masson [UNESP]2022-04-28T19:55:27Z2022-04-28T19:55:27Z2002-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1160-1165Proceedings of the International Joint Conference on Neural Networks, v. 2, p. 1160-1165.http://hdl.handle.net/11449/2242382-s2.0-0036086668Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Joint Conference on Neural Networksinfo:eu-repo/semantics/openAccess2024-06-28T13:34:35Zoai:repositorio.unesp.br:11449/224238Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T13:53:00.911688Repositório 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 Nunes da Silva, Ivan [UNESP] |
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 |
Nunes da Silva, Ivan [UNESP] |
author_facet |
Nunes da Silva, Ivan [UNESP] Nepomuceno, Leonardo [UNESP] Bastos, Thiago Masson [UNESP] |
author_role |
author |
author2 |
Nepomuceno, Leonardo [UNESP] Bastos, Thiago Masson [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Nunes da Silva, Ivan [UNESP] Nepomuceno, Leonardo [UNESP] Bastos, Thiago Masson [UNESP] |
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 2022-04-28T19:55:27Z 2022-04-28T19:55:27Z |
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 |
Proceedings of the International Joint Conference on Neural Networks, v. 2, p. 1160-1165. http://hdl.handle.net/11449/224238 2-s2.0-0036086668 |
identifier_str_mv |
Proceedings of the International Joint Conference on Neural Networks, v. 2, p. 1160-1165. 2-s2.0-0036086668 |
url |
http://hdl.handle.net/11449/224238 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the International Joint Conference on Neural Networks |
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.source.none.fl_str_mv |
Scopus 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_ |
1808128286223499264 |