Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System

Endalew Ayenew Haile; Getachew Biru Worku; Asrat Mulatu Beyene; Milkias Berhanu Tuka

doi:doi:10.11648/j.acis.20210901.13

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Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System

Endalew Ayenew Haile, Getachew Biru Worku, Asrat Mulatu Beyene, Milkias Berhanu Tuka

Published in Automation, Control and Intelligent Systems (Volume 9, Issue 1)

Received: 6 January 2021 Accepted: 16 January 2021 Published: 25 January 2021

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Abstract

Power capturing capacity is one of the key performance indicators of wind turbines. This article presents a study done on the optimization of output power of upwind horizontal axis wind turbine using artificially intelligent control system. The study shows how blade tip speed ratio (λ) and pitch angle (β) are optimized to increase wind turbines power conversion coefficient (C_p) which increases the output power. An artificial intelligence system named Mandani fuzzy inference system (MFIS) was applied to optimize the power conversion coefficient in combination with blade pitch actuator control. To this end, a novel optimization technique is designed that maximizes the power harvesting ability of wind turbines by updating the parameters of the membership functions of fuzzy logic found in the MFIS. With the application of this optimization method, a power conversion coefficient C_p of 0.5608 value is achieved at optimal values of λ and β. As a result, the energy harvesting ability of the wind turbine considered is improved by 16.74%. This study clearly shows that the wind energy harvesting capacity of wind turbines can be enhanced via optimization techniques that could be further implemented in wind turbine blade pitch drive system. Thus, this novel optimization method creates further insights for the wind energy industry in reducing the cost of energy generation.

Published in	Automation, Control and Intelligent Systems (Volume 9, Issue 1)
DOI	10.11648/j.acis.20210901.13
Page(s)	6-21
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Energy Maximization, Upwind HAWT, MFIS, Wind Turbine Performance

References

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APA Style

Endalew Ayenew Haile, Getachew Biru Worku, Asrat Mulatu Beyene, Milkias Berhanu Tuka. (2021). Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System. Automation, Control and Intelligent Systems, 9(1), 6-21. https://doi.org/10.11648/j.acis.20210901.13

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ACS Style

Endalew Ayenew Haile; Getachew Biru Worku; Asrat Mulatu Beyene; Milkias Berhanu Tuka. Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System. Autom. Control Intell. Syst. 2021, 9(1), 6-21. doi: 10.11648/j.acis.20210901.13

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AMA Style

Endalew Ayenew Haile, Getachew Biru Worku, Asrat Mulatu Beyene, Milkias Berhanu Tuka. Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System. Autom Control Intell Syst. 2021;9(1):6-21. doi: 10.11648/j.acis.20210901.13

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@article{10.11648/j.acis.20210901.13,
  author = {Endalew Ayenew Haile and Getachew Biru Worku and Asrat Mulatu Beyene and Milkias Berhanu Tuka},
  title = {Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System},
  journal = {Automation, Control and Intelligent Systems},
  volume = {9},
  number = {1},
  pages = {6-21},
  doi = {10.11648/j.acis.20210901.13},
  url = {https://doi.org/10.11648/j.acis.20210901.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20210901.13},
  abstract = {Power capturing capacity is one of the key performance indicators of wind turbines. This article presents a study done on the optimization of output power of upwind horizontal axis wind turbine using artificially intelligent control system. The study shows how blade tip speed ratio (λ) and pitch angle (β) are optimized to increase wind turbines power conversion coefficient (Cp) which increases the output power. An artificial intelligence system named Mandani fuzzy inference system (MFIS) was applied to optimize the power conversion coefficient in combination with blade pitch actuator control. To this end, a novel optimization technique is designed that maximizes the power harvesting ability of wind turbines by updating the parameters of the membership functions of fuzzy logic found in the MFIS. With the application of this optimization method, a power conversion coefficient Cp of 0.5608 value is achieved at optimal values of λ and β. As a result, the energy harvesting ability of the wind turbine considered is improved by 16.74%. This study clearly shows that the wind energy harvesting capacity of wind turbines can be enhanced via optimization techniques that could be further implemented in wind turbine blade pitch drive system. Thus, this novel optimization method creates further insights for the wind energy industry in reducing the cost of energy generation.},
 year = {2021}
}

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TY  - JOUR
T1  - Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System
AU  - Endalew Ayenew Haile
AU  - Getachew Biru Worku
AU  - Asrat Mulatu Beyene
AU  - Milkias Berhanu Tuka
Y1  - 2021/01/25
PY  - 2021
N1  - https://doi.org/10.11648/j.acis.20210901.13
DO  - 10.11648/j.acis.20210901.13
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 6
EP  - 21
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20210901.13
AB  - Power capturing capacity is one of the key performance indicators of wind turbines. This article presents a study done on the optimization of output power of upwind horizontal axis wind turbine using artificially intelligent control system. The study shows how blade tip speed ratio (λ) and pitch angle (β) are optimized to increase wind turbines power conversion coefficient (Cp) which increases the output power. An artificial intelligence system named Mandani fuzzy inference system (MFIS) was applied to optimize the power conversion coefficient in combination with blade pitch actuator control. To this end, a novel optimization technique is designed that maximizes the power harvesting ability of wind turbines by updating the parameters of the membership functions of fuzzy logic found in the MFIS. With the application of this optimization method, a power conversion coefficient Cp of 0.5608 value is achieved at optimal values of λ and β. As a result, the energy harvesting ability of the wind turbine considered is improved by 16.74%. This study clearly shows that the wind energy harvesting capacity of wind turbines can be enhanced via optimization techniques that could be further implemented in wind turbine blade pitch drive system. Thus, this novel optimization method creates further insights for the wind energy industry in reducing the cost of energy generation.
VL  - 9
IS  - 1
ER  -

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Author Information

Endalew Ayenew Haile

Center of Excellence for Sustainable Energy, College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia
Getachew Biru Worku

Electrical Power and Control Engineering, School of Electrical and Computer Engineering, Adama Science and Technology University, Adama, Ethiopia
Asrat Mulatu Beyene

Center of Excellence for Sustainable Energy, College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia
Milkias Berhanu Tuka

Electrical Power and Control Engineering, School of Electrical and Computer Engineering, Adama Science and Technology University, Adama, Ethiopia

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APA Style

Endalew Ayenew Haile, Getachew Biru Worku, Asrat Mulatu Beyene, Milkias Berhanu Tuka. (2021). Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System. Automation, Control and Intelligent Systems, 9(1), 6-21. https://doi.org/10.11648/j.acis.20210901.13

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ACS Style

Endalew Ayenew Haile; Getachew Biru Worku; Asrat Mulatu Beyene; Milkias Berhanu Tuka. Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System. Autom. Control Intell. Syst. 2021, 9(1), 6-21. doi: 10.11648/j.acis.20210901.13

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AMA Style

Endalew Ayenew Haile, Getachew Biru Worku, Asrat Mulatu Beyene, Milkias Berhanu Tuka. Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System. Autom Control Intell Syst. 2021;9(1):6-21. doi: 10.11648/j.acis.20210901.13

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@article{10.11648/j.acis.20210901.13,
  author = {Endalew Ayenew Haile and Getachew Biru Worku and Asrat Mulatu Beyene and Milkias Berhanu Tuka},
  title = {Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System},
  journal = {Automation, Control and Intelligent Systems},
  volume = {9},
  number = {1},
  pages = {6-21},
  doi = {10.11648/j.acis.20210901.13},
  url = {https://doi.org/10.11648/j.acis.20210901.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20210901.13},
  abstract = {Power capturing capacity is one of the key performance indicators of wind turbines. This article presents a study done on the optimization of output power of upwind horizontal axis wind turbine using artificially intelligent control system. The study shows how blade tip speed ratio (λ) and pitch angle (β) are optimized to increase wind turbines power conversion coefficient (Cp) which increases the output power. An artificial intelligence system named Mandani fuzzy inference system (MFIS) was applied to optimize the power conversion coefficient in combination with blade pitch actuator control. To this end, a novel optimization technique is designed that maximizes the power harvesting ability of wind turbines by updating the parameters of the membership functions of fuzzy logic found in the MFIS. With the application of this optimization method, a power conversion coefficient Cp of 0.5608 value is achieved at optimal values of λ and β. As a result, the energy harvesting ability of the wind turbine considered is improved by 16.74%. This study clearly shows that the wind energy harvesting capacity of wind turbines can be enhanced via optimization techniques that could be further implemented in wind turbine blade pitch drive system. Thus, this novel optimization method creates further insights for the wind energy industry in reducing the cost of energy generation.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System
AU  - Endalew Ayenew Haile
AU  - Getachew Biru Worku
AU  - Asrat Mulatu Beyene
AU  - Milkias Berhanu Tuka
Y1  - 2021/01/25
PY  - 2021
N1  - https://doi.org/10.11648/j.acis.20210901.13
DO  - 10.11648/j.acis.20210901.13
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 6
EP  - 21
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20210901.13
AB  - Power capturing capacity is one of the key performance indicators of wind turbines. This article presents a study done on the optimization of output power of upwind horizontal axis wind turbine using artificially intelligent control system. The study shows how blade tip speed ratio (λ) and pitch angle (β) are optimized to increase wind turbines power conversion coefficient (Cp) which increases the output power. An artificial intelligence system named Mandani fuzzy inference system (MFIS) was applied to optimize the power conversion coefficient in combination with blade pitch actuator control. To this end, a novel optimization technique is designed that maximizes the power harvesting ability of wind turbines by updating the parameters of the membership functions of fuzzy logic found in the MFIS. With the application of this optimization method, a power conversion coefficient Cp of 0.5608 value is achieved at optimal values of λ and β. As a result, the energy harvesting ability of the wind turbine considered is improved by 16.74%. This study clearly shows that the wind energy harvesting capacity of wind turbines can be enhanced via optimization techniques that could be further implemented in wind turbine blade pitch drive system. Thus, this novel optimization method creates further insights for the wind energy industry in reducing the cost of energy generation.
VL  - 9
IS  - 1
ER  -

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