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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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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. |
format | Online Article Text |
id | pubmed-8564570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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