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Dynamic wake tracking using a cost-effective LiDAR and Kalman filtering: Design, simulation and full-scale validation

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  • Lio, Wai Hou
  • Larsen, Gunner Chr.
  • Thorsen, Gunhild R.

Abstract

Wind turbines in a wind farm typically operate in the wake of other turbines, inevitably leading to a power loss and enhanced structural degradation of turbines downstream. Knowledge of the wake characteristics such as position and magnitude is valuable for optimising wind farm operations. An expensive multi-beam scanning Light Detection And Ranging system (LiDAR) can easily track and characterise the wake; however, this task is non-trivial for a cost-effective LiDAR with solely a few fixed laser beams. Therefore, this paper presents a dynamic wake tracking algorithm well-suited for a cost-effective LiDAR. The proposed algorithm estimates the lateral and vertical wake-centre positions by exploiting the wake meandering dynamics and Kalman filtering. Numerical simulation results showed that the wake tracking performance by the proposed method was remarkably successful in the low turbulent wind field, and robust to any changes in the vertical mean wind shear. Similarly, in full-scale validation, the proposed algorithm using a fixed beam LiDAR demonstrated its reliable wake tracking capability that surprisingly was as good as traditional methods based on a multi-beam scanning LiDAR. Thus, the proposed algorithm presents a cost-effective alternative to track the wake movement, which is particularly valuable for numerous applications, for example, closed-loop wake steering control.

Suggested Citation

  • Lio, Wai Hou & Larsen, Gunner Chr. & Thorsen, Gunhild R., 2021. "Dynamic wake tracking using a cost-effective LiDAR and Kalman filtering: Design, simulation and full-scale validation," Renewable Energy, Elsevier, vol. 172(C), pages 1073-1086.
    • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:1073-1086
    DOI: 10.1016/j.renene.2021.03.081
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    References listed on IDEAS

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    1. Bottasso, C.L. & Cacciola, S. & Schreiber, J., 2018. "Local wind speed estimation, with application to wake impingement detection," Renewable Energy, Elsevier, vol. 116(PA), pages 155-168.
    2. Fleming, Paul A. & Gebraad, Pieter M.O. & Lee, Sang & van Wingerden, Jan-Willem & Johnson, Kathryn & Churchfield, Matt & Michalakes, John & Spalart, Philippe & Moriarty, Patrick, 2014. "Evaluating techniques for redirecting turbine wakes using SOWFA," Renewable Energy, Elsevier, vol. 70(C), pages 211-218.
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    1. Hou, Guolian & Huang, Ting & Zheng, Fumeng & Gong, Linjuan & Huang, Congzhi & Zhang, Jianhua, 2023. "Application of multi-agent EADRC in flexible operation of combined heat and power plant considering carbon emission and economy," Energy, Elsevier, vol. 263(PB).

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