Designing a modified Hopfield network to solve an economic dispatch problem with nonlinear cost function

Bibliographic Details
Main Author: Nunes da Silva, Ivan [UNESP]
Publication Date: 2002
Other Authors: Nepomuceno, Leonardo [UNESP], Bastos, Thiago Masson [UNESP]
Format: Conference object
Language: eng
Source: Repositório Institucional da UNESP
Download full: http://hdl.handle.net/11449/224238
Summary: 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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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.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/openAccess2022-04-28T19:55:27Zoai:repositorio.unesp.br:11449/224238Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:55:27Repositório 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
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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
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dc.format.none.fl_str_mv 1160-1165
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