IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i17p6394-d1232303.html
   My bibliography  Save this article

Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review

Author

Listed:
  • Amira Elkodama

    (Faculty of Engineering and Technology, Future University in Egypt (FUE), 5th Settlement, New Cairo 11835, Egypt)

  • Amr Ismaiel

    (Faculty of Engineering and Technology, Future University in Egypt (FUE), 5th Settlement, New Cairo 11835, Egypt)

  • A. Abdellatif

    (Mechanical Engineering Department, Arab Academy for Science Technology and Maritime Transport, Cairo 11799, Egypt)

  • S. Shaaban

    (Mechanical Engineering Department, Arab Academy for Science Technology and Maritime Transport, Cairo 11799, Egypt)

  • Shigeo Yoshida

    (Research Institute for Applied Mechanics (RIAM), Kyushu University, 6-1 Kasugakoen, Kasuga 816-8580, Japan
    Institute of Ocean Energy (IOES), Saga University, Saga 840-8502, Japan)

  • Mostafa A. Rushdi

    (Research Institute for Applied Mechanics (RIAM), Kyushu University, 6-1 Kasugakoen, Kasuga 816-8580, Japan)

Abstract

In recent years, the increasing environmental problems, especially the issue of global warming, have motivated demand for a cleaner, more sustainable, and economically viable energy source. In this context, wind energy plays a significant role due to the small negative impact it has on the environment, which makes it among the most widespread potential sustainable renewable fuel nowadays. However, wind turbine control systems are important factors in determining the efficiency and cost-effectiveness of a wind turbine (WT) system for wind applications. As wind turbines become more flexible and larger, it is difficult to develop a control algorithm that guarantees both efficiency and reliability as these are conflicting objectives. This paper reviews various control strategies for the three main control systems of WT, which are pitch, torque, and yaw control, in different operational regions considering multi-objective control techniques. The different control algorithms are generally categorized as classical, modern (soft computing) and artificial intelligence (AI) for each WT control system. Modern and soft computing techniques have been showing remarkable improvement in system performance with minimal cost and faster response. For pitch and yaw systems, soft computing control algorithms like fuzzy logic control (FLC), sliding mode control (SMC), and maximum power point tracking (MPPT) showed superior performance and enhanced the WT power performance by up to 5% for small-scale WTs and up to 2% for multi-megawatt WTs. For torque control systems, direct torque control (DTC) and MPPT AI-based techniques were suitable for reducing generator torque fluctuations and estimating the torque coefficient for different wind speed regions. Classical control techniques such as PI/PID resulted in poor dynamic response for large-scale WTs. However, to improve classical control techniques, AI algorithms could be used to tune the controller’s parameters to enhance its response, as a WT is a highly non-linear system. A graphical abstract is presented at the end of the paper showing the pros/cons of each control system category regarding each WT control system.

Suggested Citation

  • Amira Elkodama & Amr Ismaiel & A. Abdellatif & S. Shaaban & Shigeo Yoshida & Mostafa A. Rushdi, 2023. "Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-32, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6394-:d:1232303
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/17/6394/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/17/6394/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lasheen, Ahmed & Elshafei, Abdel Latif, 2016. "Wind-turbine collective-pitch control via a fuzzy predictive algorithm," Renewable Energy, Elsevier, vol. 87(P1), pages 298-306.
    2. Davide Astolfi & Francesco Castellani & Matteo Becchetti & Andrea Lombardi & Ludovico Terzi, 2020. "Wind Turbine Systematic Yaw Error: Operation Data Analysis Techniques for Detecting It and Assessing Its Performance Impact," Energies, MDPI, vol. 13(9), pages 1-17, May.
    3. Yarong Zou & Wen Tan & Xingkang Jin & Zijian Wang, 2022. "An Active Disturbance Rejection Control of Large Wind Turbine Pitch Angle Based on Extremum-Seeking Algorithm," Energies, MDPI, vol. 15(8), pages 1-15, April.
    4. Song, Dongran & Fan, Xinyu & Yang, Jian & Liu, Anfeng & Chen, Sifan & Joo, Young Hoon, 2018. "Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method," Applied Energy, Elsevier, vol. 224(C), pages 267-279.
    5. Golnary, Farshad & Tse, K.T., 2021. "Novel sensorless fault-tolerant pitch control of a horizontal axis wind turbine with a new hybrid approach for effective wind velocity estimation," Renewable Energy, Elsevier, vol. 179(C), pages 1291-1315.
    6. Deng, Xiaofei & Yang, Jian & Sun, Yao & Song, Dongran & Xiang, Xiaoyan & Ge, Xiaohai & Joo, Young Hoon, 2019. "Sensorless effective wind speed estimation method based on unknown input disturbance observer and extreme learning machine," Energy, Elsevier, vol. 186(C).
    7. Lucky Dube & Graham C. Garner & Karen S. Garner & Maarten J. Kamper, 2023. "Simple and Robust MPPT Current Control of a Wound Rotor Synchronous Wind Generator," Energies, MDPI, vol. 16(7), pages 1-21, April.
    8. Song, Dongran & Tu, Yanping & Wang, Lei & Jin, Fangjun & Li, Ziqun & Huang, Chaoneng & Xia, E & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Hoon Joo, Young, 2022. "Coordinated optimization on energy capture and torque fluctuation of wind turbines via variable weight NMPC with fuzzy regulator," Applied Energy, Elsevier, vol. 312(C).
    9. Song, Dongran & Yang, Jian & Dong, Mi & Joo, Young Hoon, 2017. "Model predictive control with finite control set for variable-speed wind turbines," Energy, Elsevier, vol. 126(C), pages 564-572.
    10. Song, Dongran & Li, Ziqun & Wang, Lei & Jin, Fangjun & Huang, Chaoneng & Xia, E. & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Joo, Young Hoon, 2022. "Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation," Applied Energy, Elsevier, vol. 312(C).
    11. Gao, Richie & Gao, Zhiwei, 2016. "Pitch control for wind turbine systems using optimization, estimation and compensation," Renewable Energy, Elsevier, vol. 91(C), pages 501-515.
    12. Aitor Saenz-Aguirre & Ekaitz Zulueta & Unai Fernandez-Gamiz & Javier Lozano & Jose Manuel Lopez-Guede, 2019. "Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control," Energies, MDPI, vol. 12(3), pages 1-17, January.
    13. Jing, Bo & Qian, Zheng & Pei, Yan & Zhang, Lizhong & Yang, Tingyi, 2020. "Improving wind turbine efficiency through detection and calibration of yaw misalignment," Renewable Energy, Elsevier, vol. 160(C), pages 1217-1227.
    14. Ahmed G. Abo-Khalil & Saeed Alyami & Khairy Sayed & Ayman Alhejji, 2019. "Dynamic Modeling of Wind Turbines Based on Estimated Wind Speed under Turbulent Conditions," Energies, MDPI, vol. 12(10), pages 1-25, May.
    15. Jauch, Clemens & Islam, Syed M. & Sørensen, Poul & Bak Jensen, Birgitte, 2007. "Design of a wind turbine pitch angle controller for power system stabilisation," Renewable Energy, Elsevier, vol. 32(14), pages 2334-2349.
    16. Srikanth Bashetty & Joaquin I. Guillamon & Shanmukha S. Mutnuri & Selahattin Ozcelik, 2020. "Design of a Robust Adaptive Controller for the Pitch and Torque Control of Wind Turbines," Energies, MDPI, vol. 13(5), pages 1-22, March.
    17. Duong, Minh Quan & Grimaccia, Francesco & Leva, Sonia & Mussetta, Marco & Ogliari, Emanuele, 2014. "Pitch angle control using hybrid controller for all operating regions of SCIG wind turbine system," Renewable Energy, Elsevier, vol. 70(C), pages 197-203.
    18. Aitor Saenz-Aguirre & Unai Fernandez-Gamiz & Ekaitz Zulueta & Alain Ulazia & Jon Martinez-Rico, 2019. "Optimal Wind Turbine Operation by Artificial Neural Network-Based Active Gurney Flap Flow Control," Sustainability, MDPI, vol. 11(10), pages 1-17, May.
    19. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    20. Barambones, Oscar & Cortajarena, Jose A. & Calvo, Isidro & Gonzalez de Durana, Jose M. & Alkorta, Patxi & Karami-Mollaee, A., 2019. "Variable speed wind turbine control scheme using a robust wind torque estimation," Renewable Energy, Elsevier, vol. 133(C), pages 354-366.
    21. Venkaiah, P. & Sarkar, Bikash K., 2020. "Hydraulically actuated horizontal axis wind turbine pitch control by model free adaptive controller," Renewable Energy, Elsevier, vol. 147(P1), pages 55-68.
    22. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    23. Sungsu Park & Yoonsu Nam, 2012. "Two LQRI based Blade Pitch Controls for Wind Turbines," Energies, MDPI, vol. 5(6), pages 1-19, June.
    24. Pan, Lin & Wang, Xudong, 2020. "Variable pitch control on direct-driven PMSG for offshore wind turbine using Repetitive-TS fuzzy PID control," Renewable Energy, Elsevier, vol. 159(C), pages 221-237.
    25. Xie, Jingjie & Dong, Hongyang & Zhao, Xiaowei, 2023. "Data-driven torque and pitch control of wind turbines via reinforcement learning," Renewable Energy, Elsevier, vol. 215(C).
    26. Jia, Chengzhen & Wang, Lingmei & Meng, Enlong & Chen, Liming & Liu, Yushan & Jia, Wenqiang & Bao, Yutao & Liu, Zhenguo, 2021. "Combining LIDAR and LADRC for intelligent pitch control of wind turbines," Renewable Energy, Elsevier, vol. 169(C), pages 1091-1105.
    27. Boukhezzar, B. & Lupu, L. & Siguerdidjane, H. & Hand, M., 2007. "Multivariable control strategy for variable speed, variable pitch wind turbines," Renewable Energy, Elsevier, vol. 32(8), pages 1273-1287.
    28. Wim Munters & Johan Meyers, 2018. "Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization," Energies, MDPI, vol. 11(1), pages 1-32, January.
    29. Guo, Peng & Chen, Si & Chu, Jingchun & Infield, David, 2020. "Wind direction fluctuation analysis for wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1026-1035.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Djamel Ikni & Elena Raducan, 2024. "Stability Analysis of a Wind Turbine Controlled by Direct Torque Control," Energies, MDPI, vol. 17(11), pages 1-20, May.
    2. Ali Fayazi & Hossein Ghayoumi Zadeh & Hossein Ahmadian & Mahdi Ghane & Omid Rahmani Seryasat, 2024. "Pitch Actuator Fault-Tolerant Control of Wind Turbines via an L 1 Adaptive Sliding Mode Control ( SMC ) Scheme," Energies, MDPI, vol. 17(16), pages 1-20, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Golnary, Farshad & Tse, K.T., 2021. "Novel sensorless fault-tolerant pitch control of a horizontal axis wind turbine with a new hybrid approach for effective wind velocity estimation," Renewable Energy, Elsevier, vol. 179(C), pages 1291-1315.
    2. Shrabani Sahu & Sasmita Behera, 2022. "A review on modern control applications in wind energy conversion system," Energy & Environment, , vol. 33(2), pages 223-262, March.
    3. Tiwari, Ramji & Babu, N. Ramesh, 2016. "Recent developments of control strategies for wind energy conversion system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 268-285.
    4. Liu, Junbo & Cai, Chang & Song, Dongran & Zhong, Xiaohui & Shi, Kezhong & Chen, Yinpeng & Cheng, Shijie & Huang, Yupian & Jiang, Xue & Li, Qing'an, 2024. "Nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model," Energy, Elsevier, vol. 311(C).
    5. Azizi, Askar & Nourisola, Hamid & Shoja-Majidabad, Sajjad, 2019. "Fault tolerant control of wind turbines with an adaptive output feedback sliding mode controller," Renewable Energy, Elsevier, vol. 135(C), pages 55-65.
    6. Palanimuthu, Kumarasamy & Joo, Young Hoon, 2023. "Reliability improvement of the large-scale wind turbines with actuator faults using a robust fault-tolerant synergetic pitch control," Renewable Energy, Elsevier, vol. 217(C).
    7. Gao, Richie & Gao, Zhiwei, 2016. "Pitch control for wind turbine systems using optimization, estimation and compensation," Renewable Energy, Elsevier, vol. 91(C), pages 501-515.
    8. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    9. Song, Dongran & Liu, Junbo & Yang, Yinggang & Yang, Jian & Su, Mei & Wang, Yun & Gui, Ning & Yang, Xuebing & Huang, Lingxiang & Hoon Joo, Young, 2021. "Maximum wind energy extraction of large-scale wind turbines using nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm," Energy, Elsevier, vol. 221(C).
    10. Kumarasamy Palanimuthu & Ganesh Mayilsamy & Ameerkhan Abdul Basheer & Seong-Ryong Lee & Dongran Song & Young Hoon Joo, 2022. "A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems," Energies, MDPI, vol. 15(21), pages 1-27, November.
    11. Zhu, Xiaoxun & Chen, Yao & Xu, Shinai & Zhang, Shaohai & Gao, Xiaoxia & Sun, Haiying & Wang, Yu & Zhao, Fei & Lv, Tiancheng, 2023. "Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification," Energy, Elsevier, vol. 270(C).
    12. Xiaodong Wang & Zhaoliang Ye & Shun Kang & Hui Hu, 2019. "Investigations on the Unsteady Aerodynamic Characteristics of a Horizontal-Axis Wind Turbine during Dynamic Yaw Processes," Energies, MDPI, vol. 12(16), pages 1-23, August.
    13. Subbulakshmi, A. & Verma, Mohit & Keerthana, M. & Sasmal, Saptarshi & Harikrishna, P. & Kapuria, Santosh, 2022. "Recent advances in experimental and numerical methods for dynamic analysis of floating offshore wind turbines — An integrated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    14. Chan Roh, 2022. "Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators," Energies, MDPI, vol. 15(9), pages 1-18, April.
    15. Ganesh Mayilsamy & Kumarasamy Palanimuthu & Raghul Venkateswaran & Ruban Periyanayagam Antonysamy & Seong Ryong Lee & Dongran Song & Young Hoon Joo, 2023. "A Review of State Estimation Techniques for Grid-Connected PMSG-Based Wind Turbine Systems," Energies, MDPI, vol. 16(2), pages 1-27, January.
    16. Yanfang Chen & Young Hoon Joo & Dongran Song, 2022. "Multi-Objective Optimisation for Large-Scale Offshore Wind Farm Based on Decoupled Groups Operation," Energies, MDPI, vol. 15(7), pages 1-24, March.
    17. Chen, Peng & Han, Dezhi, 2022. "Effective wind speed estimation study of the wind turbine based on deep learning," Energy, Elsevier, vol. 247(C).
    18. Bashir, Hassan & Sibtain, Muhammad & Hanay, Özge & Azam, Muhammad Imran & Qurat-ul-Ain, & Saleem, Snoober, 2023. "Decomposition and Harris hawks optimized multivariate wind speed forecasting utilizing sequence2sequence-based spatiotemporal attention," Energy, Elsevier, vol. 278(PB).
    19. Li, Tenghui & Yang, Jin & Ioannou, Anastasia, 2024. "Data-driven control of wind turbine under online power strategy via deep learning and reinforcement learning," Renewable Energy, Elsevier, vol. 234(C).
    20. Zhicheng Lin & Song Zheng & Zhicheng Chen & Rong Zheng & Wang Zhang, 2019. "Application Research of the Parallel System Theory and the Data Engine Approach in Wind Energy Conversion System," Energies, MDPI, vol. 12(5), pages 1-20, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6394-:d:1232303. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.