Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization

To achieve the maximum power from wind in variable-speed regions of wind turbines (WTs), a suitable control signal should be applied to the pitch angle of the blades. However, the available uncertainty in the modeling of WTs complicates calculations of these signals. To cope with this problem, an op...

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Detalles Bibliográficos
Autores: Karami-Mollaee, Ali, Barambones Caramazana, Oscar
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/59414
Acceso en línea:http://hdl.handle.net/10810/59414
Access Level:acceso abierto
Palabra clave:wind turbine
pitch angle control
fractional particle swarm optimization
fuzzy inference system
Taylor series
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spelling Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm OptimizationKarami-Mollaee, AliBarambones Caramazana, Oscarwind turbinepitch angle controlfractional particle swarm optimizationfuzzy inference systemTaylor seriesTo achieve the maximum power from wind in variable-speed regions of wind turbines (WTs), a suitable control signal should be applied to the pitch angle of the blades. However, the available uncertainty in the modeling of WTs complicates calculations of these signals. To cope with this problem, an optimal controller is suitable, such as particle swarm optimization (PSO). To improve the performance of the controller, fractional order PSO (FPSO) is proposed and implemented. In order to construct this approach for a two-mass WT, we propose a new state feedback, which was first applied to the turbine. The idea behind this state feedback was based on the Taylor series. Then, a linear model with uncertainty was obtained with a new input control signal. Thereafter, the conventional PSO (CPSO) and FPSO were used as optimal controllers for the resulting linear model. Finally, a comparison was performed between CPSO and FPSO and the fuzzy Takagi–Sugeno–Kang (TSK) inference system. The provided comparison demonstrates the advantages of the Taylor series with combination to these controllers. Notably, without the state feedback, CPSO, FPSO, and TSK fuzzy systems cannot stabilize WTs in tracking the desired trajectory.MDPI2023202320222023info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/59414reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.mdpi.com/2075-1680/12/1/25info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/© 2022 by the authors.Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).oai:addi.ehu.eus:10810/594142026-06-18T09:23:17Z
dc.title.none.fl_str_mv Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization
title Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization
spellingShingle Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization
Karami-Mollaee, Ali
wind turbine
pitch angle control
fractional particle swarm optimization
fuzzy inference system
Taylor series
title_short Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization
title_full Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization
title_fullStr Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization
title_full_unstemmed Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization
title_sort Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization
dc.creator.none.fl_str_mv Karami-Mollaee, Ali
Barambones Caramazana, Oscar
author Karami-Mollaee, Ali
author_facet Karami-Mollaee, Ali
Barambones Caramazana, Oscar
author_role author
author2 Barambones Caramazana, Oscar
author2_role author
dc.subject.none.fl_str_mv wind turbine
pitch angle control
fractional particle swarm optimization
fuzzy inference system
Taylor series
topic wind turbine
pitch angle control
fractional particle swarm optimization
fuzzy inference system
Taylor series
description To achieve the maximum power from wind in variable-speed regions of wind turbines (WTs), a suitable control signal should be applied to the pitch angle of the blades. However, the available uncertainty in the modeling of WTs complicates calculations of these signals. To cope with this problem, an optimal controller is suitable, such as particle swarm optimization (PSO). To improve the performance of the controller, fractional order PSO (FPSO) is proposed and implemented. In order to construct this approach for a two-mass WT, we propose a new state feedback, which was first applied to the turbine. The idea behind this state feedback was based on the Taylor series. Then, a linear model with uncertainty was obtained with a new input control signal. Thereafter, the conventional PSO (CPSO) and FPSO were used as optimal controllers for the resulting linear model. Finally, a comparison was performed between CPSO and FPSO and the fuzzy Takagi–Sugeno–Kang (TSK) inference system. The provided comparison demonstrates the advantages of the Taylor series with combination to these controllers. Notably, without the state feedback, CPSO, FPSO, and TSK fuzzy systems cannot stabilize WTs in tracking the desired trajectory.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/59414
url http://hdl.handle.net/10810/59414
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/2075-1680/12/1/25
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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