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

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

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review

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    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 using 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.
    Original languageEnglish
    JournalRenewable Energy
    Pages (from-to)1073-1086
    Number of pages14
    Publication statusPublished - 2021


    • Dynamic wake meandering
    • Kalman filtering
    • State estimation
    • Wake characterization
    • Wake detection
    • Wake steering control


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