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Wind Predictions Upstream Wind Turbines from a LiDAR Database

Author

Listed:
  • Soledad Le Clainche

    (ETSIAE (School of Aeronautics), Universidad Politécnica de Madrid, E-28040 Madrid, Spain)

  • Luis S. Lorente

    (ETSIAE (School of Aeronautics), Universidad Politécnica de Madrid, E-28040 Madrid, Spain)

  • José M. Vega

    (ETSIAE (School of Aeronautics), Universidad Politécnica de Madrid, E-28040 Madrid, Spain)

Abstract

This article presents a new method to predict the wind velocity upstream a horizontal axis wind turbine from a set of light detection and ranging (LiDAR) measurements. The method uses higher order dynamic mode decomposition (HODMD) to construct a reduced order model (ROM) that can be extrapolated in space. LiDAR measurements have been carried out upstream a wind turbine at six different planes perpendicular to the wind turbine axis. This new HODMD-based ROM predicts with high accuracy the wind velocity during a timespan of 24 h in a plane of measurements that is more than 225 m far away from the wind turbine. Moreover, the technique introduced is general and obtained with an almost negligible computational cost. This fact makes it possible to extend its application to both vertical axis wind turbines and real-time operation.

Suggested Citation

  • Soledad Le Clainche & Luis S. Lorente & José M. Vega, 2018. "Wind Predictions Upstream Wind Turbines from a LiDAR Database," Energies, MDPI, vol. 11(3), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:543-:d:134537
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    Citations

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    Cited by:

    1. Xiongyao Xie & Mingrui Zhao & Jiamin He & Biao Zhou, 2019. "Automatic and Visual Processing Method of Non-Contact Monitoring for Circular Stormwater Sewage Tunnels Based on LiDAR Data," Energies, MDPI, vol. 12(9), pages 1-19, April.
    2. Soledad Le Clainche & José M. Vega, 2018. "Analyzing Nonlinear Dynamics via Data-Driven Dynamic Mode Decomposition-Like Methods," Complexity, Hindawi, vol. 2018, pages 1-21, December.
    3. Francisco Martínez-Álvarez & Alicia Troncoso & José C. Riquelme, 2018. "Data Science and Big Data in Energy Forecasting," Energies, MDPI, vol. 11(11), pages 1-2, November.
    4. Soledad Le Clainche, 2019. "Prediction of the Optimal Vortex in Synthetic Jets," Energies, MDPI, vol. 12(9), pages 1-26, April.
    5. De Cillis, Giovanni & Cherubini, Stefania & Semeraro, Onofrio & Leonardi, Stefano & De Palma, Pietro, 2022. "The influence of incoming turbulence on the dynamic modes of an NREL-5MW wind turbine wake," Renewable Energy, Elsevier, vol. 183(C), pages 601-616.
    6. De Cillis, Giovanni & Semeraro, Onofrio & Leonardi, Stefano & De Palma, Pietro & Cherubini, Stefania, 2022. "Dynamic-mode-decomposition of the wake of the NREL-5MW wind turbine impinged by a laminar inflow," Renewable Energy, Elsevier, vol. 199(C), pages 1-10.
    7. Dongheon Shin & Kyungnam Ko, 2019. "Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement," Energies, MDPI, vol. 12(6), pages 1-15, March.
    8. Yingli Wu & Xin Li & Qingquan Liu & Guangji Tong, 2022. "The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1269-1292, December.

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