Posters

Abstract

A continuous wave (CW) Lidar, such as the ZX LiDAR, requires supplementary data from an additional wind direction sensor to confirm the true sense of wind direction. Using this data, the LiDAR performs an internal wind disambiguation process by selecting at high frequency the sense of the wind direction closest to that measured by the auxiliary sensor. The wind disambiguation algorithm developed by ZX Lidars includes features that enhance its robustness for onshore environments. In this work, we aim to adapt the algorithm to improve its suitability for offshore environments as well. In particular, the ZX wind direction algorithm incorporates built-in hysteresis for the reference direction data. Specifically, it uses a rolling mean to process the high-frequency direction data rather than directly comparing high-frequency values one-to-one with the LiDAR measurements. This helps LiDARs avoid excessive reactivity, especially in low-speed conditions and onshore areas with complex orography.  This approach can also be used for floating LiDAR systems (FLS), but it requires accounting for the dynamic coordinate system introduced by the yawing motion of the FLS. Currently, the ZX disambiguation algorithm does not address this issue. For accurate comparisons, the rolling mean of the reference direction data and the instantaneous LiDAR measurements must be aligned within the same reference system. To that end, this study proposes compensating for the FLS’s yawing motion by utilizing the built-in high-frequency compass of the LiDAR. This adjustment is expected to have the greatest impact in conditions with significant buoy yawing motion. To evaluate this adjustment, data from a measurement campaign using the EOLOS FLS200 at an exposed location in the North Sea was analyzed. The LiDAR data revealed multiple wind direction peaks, which were believed to result from the yawing motion issue. It was hypothesized that compensating for buoy yaw at the high-frequency level could correct these peaks. The study found that many of the observed peaks were effectively eliminated after implementing this compensation. Additionally, the potential impacts of this correction on wind resource assessment uncertainty were also assessed. We believe that this update to the wind direction disambiguation algorithm addresses a gap in the use of this technology in offshore environments. This improvement is expected to provide more reliable data, increase confidence in subsequent analysis, and reduce wind resource assessment uncertainty.