Presentations

Abstract

This paper presents the development and validation of a method aimed at further enhancing the high data availability of wind Lidar systems. Traditional data filtering processes within modern Lidar systems may discard valid data due to historical threshold settings, potentially leading to suboptimal data availability. The mass adoption, application and validation of wind Lidar systems has provided an extensive body of data that provides the opportunity to enhance data filtering processes. One such method, the Quality Score (QS) developed by ZX Lidars, addresses this by re-assessing filtered data using advanced interpolation techniques to identify and recover data that, while previously excluded, still meets industry-standard Key Performance Indicators (KPIs). The result is the potential to increase in data availability without compromising accuracy, making the QS method a valuable post-processing tool for wind resource assessments and site calibrations. Extensive validations across multiple global test sites, including those in the UK, Germany, and Australia, have confirmed the reliability and robustness of the QS method. This presentation will provide a detailed overview of the QS method's implementation, its validation results, and its potential impact on the wind energy sector.