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Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller

Wind energy is considered as one of the rapidest rising renewable energy systems. Thus, in this paper the wind energy performance is enhanced through using a new adaptive fractional order PI (AFOPI) blade angle controller. The AFOPI controller is based on the fractional calculus that assigns both th...

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Autores principales: Shawqran, Ahmed M., El-Marhomy, Abdallah, Attia, Mahmoud A., Abdelaziz, Almoataz Y., Alhelou, Hassan Haes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8564570/
https://www.ncbi.nlm.nih.gov/pubmed/34754978
http://dx.doi.org/10.1016/j.heliyon.2021.e08239
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author Shawqran, Ahmed M.
El-Marhomy, Abdallah
Attia, Mahmoud A.
Abdelaziz, Almoataz Y.
Alhelou, Hassan Haes
author_facet Shawqran, Ahmed M.
El-Marhomy, Abdallah
Attia, Mahmoud A.
Abdelaziz, Almoataz Y.
Alhelou, Hassan Haes
author_sort Shawqran, Ahmed M.
collection PubMed
description Wind energy is considered as one of the rapidest rising renewable energy systems. Thus, in this paper the wind energy performance is enhanced through using a new adaptive fractional order PI (AFOPI) blade angle controller. The AFOPI controller is based on the fractional calculus that assigns both the integrator order and the fractional gain. The initialization of the controller parameters and the integrator order are optimized using the Harmony search algorithm (HSA) hybrid Equilibrium optimization algorithm (EO). Then, the controller gains ([Formula: see text]) are auto-tuned. The validation of the new proposed controller is carried out through comparison with the traditional PID and the Adaptive PI controllers under normal and fault conditions. The fractional adaptive PI improved the wind turbine's electrical and mechanical behaviors. The adaptive fractional order PI controller has been subjected to other high variation wind speed profiles to prove its robustness. The controller showed robustness to the variations in wind speed profile and the nonlinearity of the system. Also, the proposed controller (AFOPI) assured continuous wind power generation under these sharp variations. Moreover, the active power statistical analysis of the AFOPI showed increase in energy captured of around 25 %, and reduction in the standard deviation and root mean square error of around 10% compared to the other controllers.
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spelling pubmed-85645702021-11-08 Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller Shawqran, Ahmed M. El-Marhomy, Abdallah Attia, Mahmoud A. Abdelaziz, Almoataz Y. Alhelou, Hassan Haes Heliyon Research Article Wind energy is considered as one of the rapidest rising renewable energy systems. Thus, in this paper the wind energy performance is enhanced through using a new adaptive fractional order PI (AFOPI) blade angle controller. The AFOPI controller is based on the fractional calculus that assigns both the integrator order and the fractional gain. The initialization of the controller parameters and the integrator order are optimized using the Harmony search algorithm (HSA) hybrid Equilibrium optimization algorithm (EO). Then, the controller gains ([Formula: see text]) are auto-tuned. The validation of the new proposed controller is carried out through comparison with the traditional PID and the Adaptive PI controllers under normal and fault conditions. The fractional adaptive PI improved the wind turbine's electrical and mechanical behaviors. The adaptive fractional order PI controller has been subjected to other high variation wind speed profiles to prove its robustness. The controller showed robustness to the variations in wind speed profile and the nonlinearity of the system. Also, the proposed controller (AFOPI) assured continuous wind power generation under these sharp variations. Moreover, the active power statistical analysis of the AFOPI showed increase in energy captured of around 25 %, and reduction in the standard deviation and root mean square error of around 10% compared to the other controllers. Elsevier 2021-10-22 /pmc/articles/PMC8564570/ /pubmed/34754978 http://dx.doi.org/10.1016/j.heliyon.2021.e08239 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Shawqran, Ahmed M.
El-Marhomy, Abdallah
Attia, Mahmoud A.
Abdelaziz, Almoataz Y.
Alhelou, Hassan Haes
Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller
title Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller
title_full Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller
title_fullStr Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller
title_full_unstemmed Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller
title_short Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller
title_sort enhancement of wind energy conversion system performance using adaptive fractional order pi blade angle controller
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8564570/
https://www.ncbi.nlm.nih.gov/pubmed/34754978
http://dx.doi.org/10.1016/j.heliyon.2021.e08239
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