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

Author

Listed:
  • 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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    References listed on IDEAS

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    Cited by:

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    2. Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2022. "Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem," Energies, MDPI, vol. 15(13), pages 1-26, June.
    3. 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.
    4. 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).
    5. 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.
    6. Gao, Xiaoxia & Zhang, Shaohai & Li, Luqing & Xu, Shinai & Chen, Yao & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu & Lu, Hao, 2022. "Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test," Energy, Elsevier, vol. 254(PA).
    7. Shu, Tong & Song, Dongran & Joo, Young Hoon, 2022. "Non-centralised coordinated optimisation for maximising offshore wind farm power via a sparse communication architecture," Applied Energy, Elsevier, vol. 324(C).
    8. Song, Dongran & Xu, Shanmin & Huang, Lingxiang & Xia, E. & Huang, Chaoneng & Yang, Jian & Hu, Yang & Fang, Fang, 2022. "Multi-site and multi-objective optimization for wind turbines based on the design of virtual representative wind farm," Energy, Elsevier, vol. 252(C).
    9. Li, Li & Dong, Mi & Song, Dongran & Yang, Jian & Wang, Qibing, 2022. "Distributed and real-time economic dispatch strategy for an islanded microgrid with fair participation of thermostatically controlled loads," Energy, Elsevier, vol. 261(PB).
    10. Tong Shu & Young Hoon Joo, 2023. "Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach," Energies, MDPI, vol. 16(20), pages 1-21, October.
    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. 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.
    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).

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