Dynamic wake tracking and characteristics estimation using a cost-effective LiDAR

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.