Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation Systems

Systems of distributed generation have shown to be a remarkable alternative to a rational use of energy. Nevertheless, the proper functioning of them still manifests a range of challenges, including both the adequate energy dispatch depending on the variability of consumption and the interaction bet...

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Main Authors: Helbert Eduardo Espitia, Iván Machón-González, Hilario López-García, Guzmán Díaz
Format: Article
Language:English
Published: Hindawi-Wiley 2019-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2019/1610898
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spelling doaj-f7ff1a0c21ec4aecb98d0ec87a65b95c2020-11-25T02:18:40ZengHindawi-WileyComplexity1076-27871099-05262019-01-01201910.1155/2019/16108981610898Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation SystemsHelbert Eduardo Espitia0Iván Machón-González1Hilario López-García2Guzmán Díaz3Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá, ColombiaDepartamento de Ingeniería Eléctrica, Electrónica de Computadores y Sistemas, Universidad de Oviedo, Campus de Viesques, Gijón/Xixón, SpainDepartamento de Ingeniería Eléctrica, Electrónica de Computadores y Sistemas, Universidad de Oviedo, Campus de Viesques, Gijón/Xixón, SpainDepartamento de Ingeniería Eléctrica, Electrónica de Computadores y Sistemas, Universidad de Oviedo, Campus de Viesques, Gijón/Xixón, SpainSystems of distributed generation have shown to be a remarkable alternative to a rational use of energy. Nevertheless, the proper functioning of them still manifests a range of challenges, including both the adequate energy dispatch depending on the variability of consumption and the interaction between generators. This paper describes the implementation of an adaptive neurofuzzy system for voltage control, regarding the changes observed in the consumption within the distribution system. The proposed design employs two neurofuzzy systems, one for the plant dynamics identification and the other for control purposes. This focus optimizes the controller using the model achieved through the identification of the plant, whose changes are produced by charge variation; consequently, this process is adaptively performed. The results show the performance of the adaptive neurofuzzy system via statistical analysis.http://dx.doi.org/10.1155/2019/1610898
collection DOAJ
language English
format Article
sources DOAJ
author Helbert Eduardo Espitia
Iván Machón-González
Hilario López-García
Guzmán Díaz
spellingShingle Helbert Eduardo Espitia
Iván Machón-González
Hilario López-García
Guzmán Díaz
Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation Systems
Complexity
author_facet Helbert Eduardo Espitia
Iván Machón-González
Hilario López-García
Guzmán Díaz
author_sort Helbert Eduardo Espitia
title Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation Systems
title_short Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation Systems
title_full Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation Systems
title_fullStr Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation Systems
title_full_unstemmed Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation Systems
title_sort proposal of an adaptive neurofuzzy system to control flow power in distributed generation systems
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2019-01-01
description Systems of distributed generation have shown to be a remarkable alternative to a rational use of energy. Nevertheless, the proper functioning of them still manifests a range of challenges, including both the adequate energy dispatch depending on the variability of consumption and the interaction between generators. This paper describes the implementation of an adaptive neurofuzzy system for voltage control, regarding the changes observed in the consumption within the distribution system. The proposed design employs two neurofuzzy systems, one for the plant dynamics identification and the other for control purposes. This focus optimizes the controller using the model achieved through the identification of the plant, whose changes are produced by charge variation; consequently, this process is adaptively performed. The results show the performance of the adaptive neurofuzzy system via statistical analysis.
url http://dx.doi.org/10.1155/2019/1610898
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AT ivanmachongonzalez proposalofanadaptiveneurofuzzysystemtocontrolflowpowerindistributedgenerationsystems
AT hilariolopezgarcia proposalofanadaptiveneurofuzzysystemtocontrolflowpowerindistributedgenerationsystems
AT guzmandiaz proposalofanadaptiveneurofuzzysystemtocontrolflowpowerindistributedgenerationsystems
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