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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. Sungsu Park & Yoonsu Nam, 2012. "Two LQRI based Blade Pitch Controls for Wind Turbines," Energies, MDPI, vol. 5(6), pages 1-19, June.
    9. 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.
    10. 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.
    11. 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).
    12. 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.
    13. 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.
    14. 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.
    15. 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).
    16. 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.
    17. 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.
    18. 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).
    19. 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.
    20. 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).
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
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