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Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation

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  • Song, Dongran
  • Li, Ziqun
  • Wang, Lei
  • Jin, Fangjun
  • Huang, Chaoneng
  • Xia, E.
  • Rizk-Allah, Rizk M.
  • Yang, Jian
  • Su, Mei
  • Joo, Young Hoon

Abstract

Wind direction is random and time-varying, which is arduous to be accurately predicted. The yaw control based on the predicted wind direction is limited by the accuracy of the wind direction prediction, which leads to narrow improvement in the energy capture efficiency of the wind turbine (WT). For this issue, a Stochastic Model Predictive Yaw Control (SMPYC) strategy based on Intelligent Scenarios Generation (ISG) is proposed. Herein, in view of the uncertainty of wind direction prediction, the ISG method is proposed to generate scenarios that characterize it, then the yaw action optimized through the proposed scenario-based SMPYC is performed to improve the energy capture efficiency of WTs. Specifically, ISG creates an optimization problem from scenarios generation in each control period, and the co-evolution bonobo optimizer is improved to solve the optimal scenarios in real time for this high-dimensional multimodal problem. The proposed SMPYC based on ISG is tested using historical wind direction data, and its effectiveness and advantages under different accuracy of wind direction prediction are validated by the test results. The proposed SMPYC reduces the yaw time ratio by 0.35%-1.58% and improves the energy capture efficiency by 0.26%-0.43% in comparison with the baseline MPYC. For a 5 MW WT, the gained energy production could reach 1.14–1.88 × 105 kWh in a year, which corresponds to an additional annual profit of 68,000–110,000 yuan. Consequently, the proposed method is promising to enhance the energy capture efficiency and has important application value for reducing the cost of wind power.

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  • 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).
  • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922002239
    DOI: 10.1016/j.apenergy.2022.118773
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    5. 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.
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    8. 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).
    9. Ahmet Selim Pehlivan & Beste Bahceci & Kemalettin Erbatur, 2022. "Genetically Optimized Pitch Angle Controller of a Wind Turbine with Fuzzy Logic Design Approach," Energies, MDPI, vol. 15(18), pages 1-15, September.
    10. 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).
    11. Wang, Yu & Wei, Shanbi & Yang, Wei & Chai, Yi, 2023. "Adaptive economic predictive control for offshore wind farm active yaw considering generation uncertainty," Applied Energy, Elsevier, vol. 351(C).
    12. 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.
    13. Ye, Lin & Peng, Yishu & Li, Yilin & Li, Zhuo, 2024. "A novel informer-time-series generative adversarial networks for day-ahead scenario generation of wind power," Applied Energy, Elsevier, vol. 364(C).
    14. Jia, Yaya & Huang, Jiachen & Liu, Qingkuan & Zhao, Zonghan & Dong, Menghui, 2024. "The wind tunnel test research on the aerodynamic stability of wind turbine airfoils," Energy, Elsevier, vol. 294(C).
    15. Liu, Xin & Yu, Jingjia & Gong, Lin & Liu, Minxia & Xiang, Xi, 2024. "A GCN-based adaptive generative adversarial network model for short-term wind speed scenario prediction," Energy, Elsevier, vol. 294(C).
    16. 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).
    17. 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.

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