Abstract
Recent developments in Light Detection And Ranging (LiDAR) systems provide the possibility of remote measurement of the upcoming wind speed. Despite significant advances in remote sensing, extracting useful inflow characteristics from a limited number of line-of-sight measurements still requires assumptions of the inflow. Typically, the wind direction is derived based on the assumption of horizontal homogeneous inflow that is well satisfied in flat terrain and over sufficiently large time averages. However, such an assumption is violated if the wake from a neighbouring turbine impinges the inflow, and the velocity deficit in the wake causes a bias on the wind direction, misinterpreting as yaw misalignment by the downstream turbine. The actual yaw misalignment can be recovered by isolating the effect of the wake velocity deficit from the ambient inflow. A scanning LiDAR can easily track and characterise the wake; however, it is non-trivial for a cost-effective LiDAR with only a few fixed laser beams. Therefore, this paper presents a dynamic wake tracking and characteristics estimation algorithm for a cost-effective LiDAR. The proposed algorithm provides estimates of the wake centre location and other wake characteristics by exploiting the nature of wake meandering dynamics and state estimation theory. Assuming neutral stratification of the atmospheric boundary layer, the simulation results show that the wake position and its characteristics estimation is achievable in full and partial wake situations, thus presenting an estimation framework for potential applications, including yaw misalignment control and wake steering control.
Highlights
In recent years, the ability to sample an oncoming wind field has been become possible with the advent of the Light Detection And Ranging (LiDAR) systems
Such an assumption is violated if the wake from a neighbouring turbine impinges the inflow, and the velocity deficit in the wake causes a bias on the wind direction, misinterpreting as yaw misalignment by the downstream turbine
This could contribute to an additional yaw error as one of the LiDAR measurements is corrupted by the wake, if the yaw controller uses the LiDAR measurements
Summary
The ability to sample an oncoming wind field has been become possible with the advent of the Light Detection And Ranging (LiDAR) systems. Wake velocity deficit model The wake velocity deficit model is presented, and such a model can be used by the estimator to identify the wake centre and other information based on the wind speed measurement from the LiDAR. Where ∆U ∈ R denotes the longitudinal velocity deficit measured at the lateral distance ym ∈ R from the hub, yw is the lateral wake centre location, whilst Upeak, σw ∈ R are the peak magnitude and standard deviation of the Gaussian wake profile. White noise, and the cut-off frequency fc ωc 2π is determined by the ambient mean wind speed U∞ ∈ R in the longitudinal direction and instantaneous wake velocity deficit diameter Dw ∈ R (details can be found in [11]): U∞ . The mean ambient velocity U∞ is currently assumed to be known, which might be formulated into the system state in the future work
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