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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Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2019/1610898 |
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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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