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Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model

Author

Listed:
  • Guangchao Zhang

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

  • Shi Liu

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

Short-term wind power forecasting is crucial for updating the wind power trading strategy, equipment protection and control regulation. To solve the difficulty surrounding the instability of the statistical model and the time-consuming nature of the physical model in short-term wind power forecasting, two innovative wind field reconstruction methods combining CFD and a reduced-order model were developed. In this study, POD and Tucker decomposition were employed to obtain the spatial–temporal information correlation of 2D and 3D wind fields, and their inverse processes were combined with sparse sensing to reconstruct multi-dimensional unsteady wind fields. Simulation and detailed discussion were performed to verify the practicability of the proposed algorithms. The simulation results indicate that the wind speed distributions could be reconstructed with reasonably high accuracy (where the absolute velocity relative error was less than 0.8%) using 20 sensors (which only accounted for 0.04% of the total data in the 3D wind field) based on the proposed algorithms. The factors influencing the results of reconstruction were systematically analyzed, including all-time steps, the number of basis vectors and 4-mode dimensions, the diversity of CFD databases, and the reconstruction time. The results indicated that the reconstruction time could be shortened to the time interval of data acquisition to synchronize data acquisition with wind field reconstruction, which is of great significance in the reconstruction of unsteady wind fields. Although there are still many studies to be carried out to achieve short-term predictions, both unsteady reconstruction methods proposed in this paper enable a new direction for short-term wind field prediction.

Suggested Citation

  • Guangchao Zhang & Shi Liu, 2023. "Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model," Mathematics, MDPI, vol. 11(10), pages 1-25, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2223-:d:1142619
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    1. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    2. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    3. Yimei Wang & Yongqian Liu & Li Li & David Infield & Shuang Han, 2018. "Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method," Energies, MDPI, vol. 11(4), pages 1-19, April.
    4. Risky Utama Putra & Hasan Basri & Akbar Teguh Prakoso & Hendri Chandra & Muhammad Imam Ammarullah & Imam Akbar & Ardiyansyah Syahrom & Tunku Kamarul, 2023. "Level of Activity Changes Increases the Fatigue Life of the Porous Magnesium Scaffold, as Observed in Dynamic Immersion Tests, over Time," Sustainability, MDPI, vol. 15(1), pages 1-13, January.
    5. Wang, Han & Han, Shuang & Liu, Yongqian & Yan, Jie & Li, Li, 2019. "Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system," Applied Energy, Elsevier, vol. 237(C), pages 1-10.
    6. Muhammad Imam Ammarullah & Gatot Santoso & S. Sugiharto & Toto Supriyono & Dwi Basuki Wibowo & Ojo Kurdi & Mohammad Tauviqirrahman & J. Jamari, 2022. "Minimizing Risk of Failure from Ceramic-on-Ceramic Total Hip Prosthesis by Selecting Ceramic Materials Based on Tresca Stress," Sustainability, MDPI, vol. 14(20), pages 1-12, October.
    7. Zhang, Guangchao & Zheng, Xiaoxiao & Liu, Shi & Chen, Minxin, 2022. "Three-dimensional wind field reconstruction using tucker decomposition with optimal sensor placement," Energy, Elsevier, vol. 260(C).
    8. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
    9. Zhang, Jie & Draxl, Caroline & Hopson, Thomas & Monache, Luca Delle & Vanvyve, Emilie & Hodge, Bri-Mathias, 2015. "Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods," Applied Energy, Elsevier, vol. 156(C), pages 528-541.
    10. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    11. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    12. Qin, Li & Liu, Shi & Long, Teng & Shahzad, Muhammad Ali & Schlaberg, H. Inaki & Yan, Song An, 2018. "Wind field reconstruction using dimension-reduction of CFD data with experimental validation," Energy, Elsevier, vol. 151(C), pages 272-288.
    13. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Li, Qi, 2019. "A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes," Applied Energy, Elsevier, vol. 233, pages 975-984.
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