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Coordinated optimization on energy capture and torque fluctuation of wind turbines via variable weight NMPC with fuzzy regulator

Author

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  • 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

Abstract

The main challenge in improving energy harvest faced by modern large-scale wind turbines (WTs) comes from the fact that the blade rotor with the large inertia having a slow response impedes the optimal tip speed ratio tracking. On the premise that the future wind speed can be accurately predicted, nonlinear model predictive control (NMPC) can effectively improve the energy harvest efficiency of large WTs, but it is difficult to maximize the energy capture and minimize the torque fluctuation of the generator simultaneously. In this study, a fuzzy regulator is designed to update the weight coefficient of the cost function, and an improved multi-objective marine predator algorithm (IMMPA) is proposed to optimize the fuzzy regulator, so it is realized the coordinated optimization on energy capture and generator torque fluctuation. Specifically, based on the analysis of the performance of NMPC with fixed weight coefficient under different wind conditions, a basic fuzzy regulator is designed to adjust the weight coefficient according to the characteristics of wind conditions, and four groups of candidate inputs are defined. In order to fully explore the potential of the fuzzy regulator, IMMPA is used to optimize the membership function of the fuzzy regulator. Finally, the variable weight coefficient is used as the input of NMPC to update the objective function in real time. The simulation results show that compared with the one with the fixed weight coefficient, the NMPC controller using the variable weight improves the energy capture by 1.77% while reducing the generator torque fluctuation by 0.126%. Taking the concerned 1.5 MW WT as an example, the variable-weight NMPC could boost its annual energy production by 35842.5 kWh in comparison with the fixed-weight counterpart, showing its promising role in reducing the production cost for the WTs.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922002665
    DOI: 10.1016/j.apenergy.2022.118821
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    1. 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.
    2. Zhang, Shizhong & Pei, Wei & Xiao, Hao & Yang, Yanhong & Ye, Hua & Kong, Li, 2020. "Enhancing the survival time of multiple islanding microgrids through composable modular energy router after natural disasters," Applied Energy, Elsevier, vol. 270(C).
    3. Hu, Lu & Xue, Fei & Qin, Zijian & Shi, Jiying & Qiao, Wen & Yang, Wenjing & Yang, Ting, 2019. "Sliding mode extremum seeking control based on improved invasive weed optimization for MPPT in wind energy conversion system," Applied Energy, Elsevier, vol. 248(C), pages 567-575.
    4. Pla, Benjamí n & Bares, Pau & Jiménez, Irina & Guardiola, Carlos & Zhang, Yahui & Shen, Tielong, 2020. "A fuzzy logic map-based knock control for spark ignition engines," Applied Energy, Elsevier, vol. 280(C).
    5. Dounis, A. I. & Manolakis, D. E., 2001. "Design of a fuzzy system for living space thermal-comfort regulation," Applied Energy, Elsevier, vol. 69(2), pages 119-144, June.
    6. Dai, Juchuan & He, Tao & Li, Mimi & Long, Xin, 2021. "Performance study of multi-source driving yaw system for aiding yaw control of wind turbines," Renewable Energy, Elsevier, vol. 163(C), pages 154-171.
    7. Bizon, Nicu, 2017. "Energy optimization of fuel cell system by using global extremum seeking algorithm," Applied Energy, Elsevier, vol. 206(C), pages 458-474.
    8. 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).
    9. Yin, Minghui & Yang, Zhiqiang & Xu, Yan & Liu, Jiankun & Zhou, Lianjun & Zou, Yun, 2018. "Aerodynamic optimization for variable-speed wind turbines based on wind energy capture efficiency," Applied Energy, Elsevier, vol. 221(C), pages 508-521.
    10. Yang, Bo & Yu, Tao & Shu, Hongchun & Dong, Jun & Jiang, Lin, 2018. "Robust sliding-mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers," Applied Energy, Elsevier, vol. 210(C), pages 711-723.
    11. El-Baklish, Shaimaa K. & El-Badawy, Ayman A. & Frison, Gianluca & Diehl, Moritz, 2020. "Nonlinear model predictive pitch control of aero-elastic wind turbine blades," Renewable Energy, Elsevier, vol. 161(C), pages 777-791.
    12. Lin, Zhongwei & Chen, Zhenyu & Liu, Jizhen & Wu, Qiuwei, 2019. "Coordinated mechanical loads and power optimization of wind energy conversion systems with variable-weight model predictive control strategy," Applied Energy, Elsevier, vol. 236(C), pages 307-317.
    13. Yao, Qi & Hu, Yang & Zhao, Tianyang & Guan, Yuanpeng & Luo, Zhiling & Liu, Jizhen, 2022. "Fatigue load suppression during active power control process in wind farm using dynamic-local-reference DMPC," Renewable Energy, Elsevier, vol. 183(C), pages 423-434.
    14. Molavi, Anahita & Shi, Jian & Wu, Yiwei & Lim, Gino J., 2020. "Enabling smart ports through the integration of microgrids: A two-stage stochastic programming approach," Applied Energy, Elsevier, vol. 258(C).
    15. Yang, Jian & Wang, Li & Song, Dongran & Huang, Chaoneng & Huang, Liansheng & Wang, Junlei, 2022. "Incorporating environmental impacts into zero-point shifting diagnosis of wind turbines yaw angle," Energy, Elsevier, vol. 238(PA).
    Full references (including those not matched with items on IDEAS)

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    10. 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).
    11. Wang, Yun & Chen, Tuo & Zou, Runmin & Song, Dongran & Zhang, Fan & Zhang, Lingjun, 2022. "Ensemble probabilistic wind power forecasting with multi-scale features," Renewable Energy, Elsevier, vol. 201(P1), pages 734-751.
    12. Xiaoxia, Gao & Luqing, Li & Shaohai, Zhang & Xiaoxun, Zhu & Haiying, Sun & Hongxing, Yang & Yu, Wang & Hao, Lu, 2022. "LiDAR-based observation and derivation of large-scale wind turbine's wake expansion model downstream of a hill," Energy, Elsevier, vol. 259(C).
    13. Pustina, L. & Biral, F. & Serafini, J., 2022. "A novel Economic Nonlinear Model Predictive Controller for power maximisation on wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    14. Mourad Yessef & Badre Bossoufi & Mohammed Taoussi & Saad Motahhir & Ahmed Lagrioui & Hamid Chojaa & Sanghun Lee & Byeong-Gwon Kang & Mohamed Abouhawwash, 2022. "Improving the Maximum Power Extraction from Wind Turbines Using a Second-Generation CRONE Controller," Energies, MDPI, vol. 15(10), pages 1-23, May.
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