High-Fidelity Processing of Instantaneous Line-of-Sight Returns from Nacelle-Mounted Lidar including Supervised Machine Learning

Abstract

Abstract. Wind turbine applications that leverage nacelle-mounted Doppler lidar are hampered by several sources of uncertainty in the lidar measurement, affecting both bias and random error. Two problems encountered especially for nacelle-mounted lidar are solid interference due to intersection of the line of sight with solid objects behind, within, or in front of the measurement volume, as well as spectral noise due primarily to limited photon capture. These two uncertainties can be reduced with high-fidelity quality assurance/quality control (QA/QC) processing techniques. Our work compares three QA/QC techniques, including conventional thresholding, advanced filtering, and a novel application of supervised machine learning with ensemble neural networks, based on their ability to reduce uncertainty introduced by the two observed non-ideal spectral features. The approach leverages data from a field experiment involving a continuous-wave (CW) SpinnerLidar from the Technical University of Denmark (DTU) that provided scans of a wide range of flows both unwaked and waked by a field turbine. Independent measurements from an overlapped meteorological tower permit experimental validation of the instantaneous velocity uncertainty remaining after QA/QC processing that stems from solid interference and strong spectral noise, which is a validation that has not been performed previously. All three methods perform similarly for non-interfered returns, but the advanced filtering and machine learning techniques perform better when solid interference is present, which allows them to produce overall standard deviations of error between 0.2 and 0.3 m/s, or a 1–22 % improvement versus the conventional thresholding technique, over the rotor height for the unwaked cases. Between the two improved techniques, the advanced filtering produces 3.5 % higher overall data availability, while the machine learning offers a faster runtime (i.e, ~1 second to evaluate) that is therefore more commensurate with the requirements of real-time turbine control. The QA/QC techniques are described in terms of application to CW lidar, though they are also relevant to pulsed lidar. Previous work by the authors (Brown and Herges, 2020) explored a novel attempt to quantify uncertainty after a lidar QA/QC process using simulated lidar returns; this article provides true uncertainty quantification versus independent measurement and does so for three rather than one QA/QC techniques.