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

| Peer-Reviewed

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2021. Published by Science Publishing Group

Keywords

Energy Maximization, Upwind HAWT, MFIS, Wind Turbine Performance

References

[1]	David A. Spera, Wind Turbine Technology: Fundamental Concepts of Wind Turbine Engineering. 2nd ed. ASME Press 2009; pp. 295-322. https://doi.org/10.1115/1.802601.
[2]	M. Ragheb, Aerodynamics of Rotor Blades, http://magdiragheb.com/NPRE%20475%20Wind%20Power%20Systems/Aerodynamics%20of%20Rotor%20Blades.pdf, 2018.
[3]	X. Jing, Modeling and control of a doubly-fed induction generator for wind turbine- generator systems. MSc Thesis, Marquette University 2012; pp. 29-30.
[4]	M. Sarvi, Sh. Abdi, S. Ahmadi, A New Method for Rapid Maximum Power Point Tracking of PMSG Wind Generator using PSO-Fuzzy Logic. Tech J Engin & App Sci. 2013; 3 (17): 1984-1995.
[5]	Jens Nørkær Sørensen, General Momentum Theory for Horizontal axis wind turbines. Research Topics in Wind Energy 4, 2016; pp. 49–85.
[6]	Robert E. Wilson, Peter B. S. Lissaman, Stel N. Walker, Aerodynamic Performance of Wind Turbines: Final Report. Energy Research and Development Administration, Division of Solar Energy, U.S. 1976; pp. 85.
[7]	Y. Sreenivasa Rao, A. Jaya Laxmi, Fuzzy logic based indirect Vector Control of Induction generator in Wind Energy Conversion System. International conference on power, Signal, Control and Computation IEEE 2012. doi: 10.1109/EPSCICON.2012.6175259.
[8]	Ranjan Vepa, Dynamic Modeling, Simulation and Control of Energy Generation. Lecture Notes in Energy, Springer-Verlag London 2013; 20: 152-183.
[9]	Ayushi Sachan, Akhilesh Kumar Gupta, Paulson Samuel, A Review of MPPT Algorithms Employed in Wind Energy Conversion Systems. Journal of Green Engineering 2016; 6 (4): 385–402. https://doi.org/10.13052/jge1904-4720.643.
[10]	Soumia EL HANI, Said GUEDIRA, Noureddine EL ALAMI, Maximum power tracking control wind turbine based on a permanent magnet synchronous generator with a complete converter. International Journal of Smart Grid and Clean Energy 2014; 3 (1): 15-21.
[11]	Jogendra Singh Thongam, Mohand Ouhrouche, MPPT Control Methods in Wind Energy Conversion Systems. Fundamental and Advanced Topics in Wind Power 2011; 15: 339- 360. DOI: 10.5772/21657.
[12]	Dinh-Chung Phan, Shigeru Yamamoto, Maximum Energy Output of a DFIG Wind Turbine Using an Improved MPPT-Curve Method. Energies 2015; 8: 11718-11736. doi: 10.3390/en81011718.
[13]	Zongze Cui, Liwei Song, Shupei Li, Maximum Power Point Tracking Strategy for a New Wind Power System and Its Design Details. IEEE Transactions on Energy Conversion 2017; 32 (3), 1063–1071. doi: 10.1109/tec.2017.2694008.
[14]	Godswill Ofualagba, Emmanuel Ubeku, Wind Energy Conversion System- Wind Turbine Modeling. IEEE Power and Energy Society General Meeting 2008; doi: 10.1109/PES.2008.4596699.
[15]	V. Kumar, R. R. Joshi, & R. C. Bansal, Optimal Control Matrix Converter based WECS for Performance Enhancement and Efficiency optimization. IEEE Transaction Energy Conversion 2009; 24 (1): 264-273. doi: 10.1109/TEC.2008.2008921.
[16]	D. Petkovic, Ž. Cojbašic, V. Nikolic, Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation. Renewable and Sustainable Energy Reviews 2013; 28 (c): 191-195. doi: 10.1016/j.rser.2013.07.049.
[17]	R. Sitharthan, T. Parthasarathy, S. Sheeba Rani, KC. Ramya, An improved radial basis function neural network control strategy-based maximum power point tracking controller for wind power generation system. Transactions of the Institute of Measurement and Control 2019; 41 (11): 1–13. https://doi.org/10.1177/0142331218823858.
[18]	L. Saad, H. Hicham, F. Khalid, Optimal Tracking, Modeling, and Control of Aero-generator based on PMSG driven by wind turbine. 5^th International Conference on Renewable Energy Research and Applications IEEE 2016; 891-896. doi: 10.1109/ICRERA.2016.7884464.
[19]	Y. Souﬁ, S. Kahla and M. Bechouat, Particle swarm optimization-based sliding mode control of variable speed wind energy conversion system. International Journal of Hydrogen Energy 2016; 41 (45): 20956-20963.
[20]	A. Ghaffari, M. Krstic, Power Optimization and Control in Wind Energy Conversion Systems Using Extremum Seeking. IEEE Transaction on Control Systems Technology 2014; 22 (5): 1684-1695. doi: 10.1109/TCST.2014.2303112.
[21]	Tony Hawkins, Maximization of Power Capture in Wind Turbines using Robust Estimation and Lyapunov Extremum Seeking Control. B.S., Kansas State University 2010; 34-62.
[22]	Vaughn N, Wind Energy Renewable Energy and the Environment. 2^nd ed. CRC Press Taylor & Francis Group LLC 2014; 115-129.
[23]	Abdel-Raheem Youssef, Hossam H. H. Mousa, Essam E. M. Mohamed, Development of Self-Adaptive P&O MPPT Algorithm for Wind Generation Systems with Concentrated Search Area, Renewable Energy. March 2020. https://doi.org/10.1016/j.renene.2020.03.050.
[24]	Wagner Barth Lenz, Angelo Marcelo Tusset, Mauricio Ap. Ribeiro, Jose Manoel Balthazar, Neuro-fuzzy control on horizontal axis wind turbine. Meccanica, January 2020. https://doi.org/10.1007/s11012-019-01118-9.
[25]	Abdel-Raheem, Youssef Mahmoud, A. Sayed M. N. Abdel-Wahab, Gaber Shabib Salman, Maximum Power Point Tracking of A Wind Power System Based on Five Phase PMSG using Optimum Torque Control.17^th MEPECON, Mansoura University, Egypt, 2015.
[26]	Hamed HABIBI, Hamed RAHIMI NOHOOJI, Ian HOWARD, Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault-tolerant control. Frontiers of Mechanical Engineering April 2017. DOI 10.1007/s11465-017-0431-4.
[27]	Hamed Habibi, Aghil Yousefi Koma, Ian Howard, Power Improvement of Non-Linear Wind Turbines during Partial Load Operation using Fuzzy Inference Control. CEAI 2017; 19 (2): 31-42.
[28]	Ali El Yaakoubi, Adel Asselman, A. Djebi, El Hassan Aroudam, A MPPT Strategy Based on Fuzzy Control for a Wind Energy Conversion System. Procedia Technology 2016; 22: 697–704. doi: 10.1016/j.protcy.2016.01.145.
[29]	Sanaz Sabzevari, Ali Karimpour, Mohammad Monfared, and Mohammad Bagher Naghibi Sistani, MPPT control of wind turbines by direct adaptive fuzzy-PI controller and using ANN-PSO wind speed estimator, Journal of Renewable Sustainable Energy 2017; 9: 013302. https://doi.org/10.1063/1.4973447
[30]	A. E. Yaakoubi, L. Amhaimar, K. Attari, M. H. Harrak, M. E. Halaoui, A. Asselman, Non-linear and intelligent maximum power point tracking strategies for small size wind turbines: Performance analysis and comparison. Energy Reports 2019; 5: 545-554. https://doi.org/10.1016/j.egyr.2019.03.001.
[31]	Gaber EL-Saady Ahmed, EL-Noby Ahmed Ibrahim, Hazem Hassan Ali, Optimum Power Point Tracking of Variable Speed Wind Turbine DFIG using Genetic Algorithm. IJETT 2016; 37 (7). DOI: 10.14445/22315381/IJETT-V37P264.
[32]	Salah Tamalouzt, Kassa Idjdarene, Toufik Rekioua, Rachid Abdessemed, Direct torque control of wind turbine driven doubly fed induction generator. Rev. Roum. Sci. Techn. Électrotechn. et Énerg. 2016; 61 (3): 244–249.
[33]	S. M. Muyeen, A. Al-Durra, J. Tamura, Variable speed wind turbine generator system with current-controlled voltage source inverter. Energy Conversion and Management 2011; 52 (7): 2688–2694. doi: 10.1016/j.enconman.2011.02.001.
[34]	L. M. Fernández, C. A. García, J. R. Saenz and F. Jurado, Equivalent models of wind farms by using aggregated wind turbines and equivalent winds. Energy Conversion and Management 2009; 50 (3): 691–704. https://doi.org/10.1016/j.enconman.2008.10.005.
[35]	Devashish, Amarnath Thakur, Swetapadma Panigrahi, R R Behera, A Review on Wind Energy Conversion System and Enabling Technology. ICEPES. IEEE 2016; 527- 532.
[36]	Nazmul Siddique, Intelligent Control-A Hybrid Approach Based on Fuzzy Logic, Neural Networks, and Genetic Algorithms. SCI, Springer International Publishing Switzerland 2014; 517: p. 79.
[37]	Ehsan Hosseini, Ghazanfar Shahgholian, Different Types of Pitch Angle Control Strategies Used in Wind Turbine System Applications. JREE 20174; 4 (1): p. 20-35. doi. 10.30501/jree.2017.70103.
[38]	Sanjay Krishnankutty Alonso, Membership Functions. eMathTeacher: Mamdani's Fuzzy Inference Method. www.dma.fi.upm.es › fuzzy › fuzzyinf › funpert_en.
[39]	Yan Shi, Masaharu Mizumoto, Naoyoshi Yubazaki, Masayuki Otani, A Learning Algorithm for Tuning Fuzzy Rules Based on the Gradient Descent Method, 1996 IEEE, pp. 55-61.
[40]	L. X, Wang, J. M. Mendel, Back-propagation fuzzy system as nonlinear dynamic system identifiers, Proceedings of the IEEE International Congress on Fuzzy Systems, San Diego, 1992, 1409-1416.
[41]	Jason Brownlee (2020) Machine Learning Mastery: Understand the Impact of Learning Rate on Neural Network Performance. https://machinelearningmastery.com.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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  -

Copy | Download

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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  -

Copy | Download