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Model predictive control with finite control set for variable-speed wind turbines

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  • Song, Dongran
  • Yang, Jian
  • Dong, Mi
  • Joo, Young Hoon

Abstract

The existing model predictive control (MPC) algorithm for variable-speed wind turbines (WTs) is using continuous control set and solved by a quadratic programming method. Its main drawbacks are the heavily computational burden and the difficulty to implement. This paper introduces an alternative MPC method by using finite control set, which is used in controlling WTs at the first attempt. To do this, first of all, the WT's nonlinear model is linearized with information provided by a non-standard extended Kalman filter. Secondly, a discrete-time linear model of the system is used to predict the future value of the interested state variable for possible control sets. In view of the fact that control objectives are different within two operation zones partitioned by wind speed, two quality functions are predefined. One quality function evaluates the optimal generator speed tracking error together with the penalty of torque actuator action at below rated wind speed, while the other evaluates the rated generator speed tracking error and the penalty of pitch actuator action at above rated wind speed. Then, the corresponding control set which minimizes the quality function is selected. Finally, some simulation results are demonstrated to visualize the effectiveness and feasibility of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:126:y:2017:i:c:p:564-572
    DOI: 10.1016/j.energy.2017.02.149
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    Cited by:

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    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.
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    13. Wakui, Tetsuya & Yoshimura, Motoki & Yokoyama, Ryohei, 2017. "Multiple-feedback control of power output and platform pitching motion for a floating offshore wind turbine-generator system," Energy, Elsevier, vol. 141(C), pages 563-578.
    14. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
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    16. Yao Liu & Lin Guan & Fang Guo & Jianping Zheng & Jianfu Chen & Chao Liu & Josep M. Guerrero, 2019. "A Reactive Power-Voltage Control Strategy of an AC Microgrid Based on Adaptive Virtual Impedance," Energies, MDPI, vol. 12(16), pages 1-15, August.
    17. Song, Dongran & Yang, Jian & Su, Mei & Liu, Anfeng & Cai, Zili & Liu, Yao & Joo, Young Hoon, 2017. "A novel wind speed estimator-integrated pitch control method for wind turbines with global-power regulation," Energy, Elsevier, vol. 138(C), pages 816-830.
    18. Hui, Jiuwu & Yuan, Jingqi, 2022. "Load following control of a pressurized water reactor via finite-time super-twisting sliding mode and extended state observer techniques," Energy, Elsevier, vol. 241(C).
    19. Jinghan Cui & Su Liu & Jinfeng Liu & Xiangjie Liu, 2018. "A Comparative Study of MPC and Economic MPC of Wind Energy Conversion Systems," Energies, MDPI, vol. 11(11), pages 1-23, November.
    20. Masood, Nahid-Al- & Mahmud, Sajjad Uddin & Ansary, Md Nazmuddoha & Deeba, Shohana Rahman, 2022. "Improvement of system strength under high wind penetration: A techno-economic assessment using synchronous condenser and SVC," Energy, Elsevier, vol. 246(C).
    21. Pires, Thiago S. & Cruz, Manuel E. & Colaço, Marcelo J. & Alves, Marco A.C., 2018. "Application of nonlinear multivariable model predictive control to transient operation of a gas turbine and NOX emissions reduction," Energy, Elsevier, vol. 149(C), pages 341-353.
    22. Xiaobing Kong & Lele Ma & Xiangjie Liu & Mohamed Abdelkarim Abdelbaky & Qian Wu, 2020. "Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions," Energies, MDPI, vol. 13(1), pages 1-21, January.
    23. Jingrong Yu & Limin Deng & Dongran Song & Maolin Pei, 2019. "Wide Bandwidth Control for Multi-Parallel Grid-Connected Inverters with Harmonic Compensation," Energies, MDPI, vol. 12(3), pages 1-22, February.
    24. Janusz Baran & Andrzej Jąderko, 2020. "An MPPT Control of a PMSG-Based WECS with Disturbance Compensation and Wind Speed Estimation," Energies, MDPI, vol. 13(23), pages 1-20, December.
    25. 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).

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