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PO085: Learnings from over 25 floating lidar validation campaigns for offshore wind resource assessment
Eric Rose, Wind Energy Researcher, TNO
Background and Motivation As wind energy continues to grow within the offshore sector, there is a need to ensure that accurate, reliable, and cheap wind resource assessments (WRA's) are possible. Floating lidar systems (FLS's) play an important role in WRA's as they are able to generate bankable data for the construction of a wind farm. These systems require pre-verification according to guidelines and standards, such as the Carbon Trust's Offshore Wind Accelerator (OWA) Roadmap, prior to use in a WRA. TNO has carried out numerous FLS validations, and in this work the results of the entirety of the campaigns are presented and analyzed. Methodology Between February 2020 and November 2021 TNO completed 27 validation campaigns for various FLS's for two companies, referred to as Brand A and B. These campaigns were performed at the Lichteiland Goeree (LEG) platform in the Dutch sector of the North Sea, where a pre-verified platform-mounted vertical profiling WindCube V2 lidar is installed, for direct comparison of the outputs. The Carbon Trust's OWA Roadmap was used in order to calculate and evaluate the key performance indicators (KPI's) for six different heights above mean sea level (AMSL). The accuracy KPI's for the wind speed and wind direction were compared between the campaigns, providing an overview of the state of the technology. Additional analysis extended to the possible influence of various outside factors on these KPI's, such as atmospheric and oceanic conditions. Results Overall the FLS's analyzed were very accurate, with almost every campaign falling within the best practice limits set by the OWA Roadmap. For each metric where these limits were breached, this tended to only be for one outlier campaign per brand. Additionally, the accuracy of the lidars were generally not correlated to many external factors. One area where a relationship can be seen is with KPI values as a function of measurement height, with almost every KPI showing some trend for both brands. For example, as measurement height increased, the wind speed correlation coefficient (R2) increased by 0.002 for Brand A, but decreased by 0.003 for Brand B. The wind speed slope did appear to have some correlation with the oceanic conditions, with correlations between 0.3 and 0.75 for the two brands of lidar. They showed increasing slope with higher wave height and period. It is expected that this could be due to either the physical construction of the FLS's, or possibly the calibration of the motion compensation. Learnings From this analysis, it can be concluded that the overall state of FLS measurements is accurate, and possibly close to reaching stage 3 maturity level as defined in the OWA Roadmap. Additionally, little influence from environmental conditions shows low sensitivity to the measurement location. FLS technology appears to be suitable for accurate WRA's, though this only holds for measurements below 200m AMSL. For WRA's that encompass the rotor diameter of offshore turbines, further investigation into the accuracy relationship with height should be undergone.