Enhanced Dual Filter for Floating Wind Lidar Motion Correction: The Impact of Wind and Initial Scan Phase Models

Abstract

An enhanced filter for floating Doppler wind lidar motion correction is presented. The filter relies on an unscented Kalman filter prototype for floating-lidar motion correction without access to the internal line-of-sight measurements of the lidar. In the present work, we implement a new architecture based on two cooperative estimation filters and study the impact of different wind and initial scan phase models on the filter performance in the coastal environment of Barcelona. Two model combinations are considered: (i) a basic random walk model for both the wind turbulence and the initial scan phase and (ii) an auto-regressive model for wind turbulence along with a uniform circular motion model for the scan phase. The filter motion-correction performance using each of the above models was evaluated with reference to a fixed lidar in different wind and motion scenarios (low- and high-frequency turbulence cases) recorded during a 25-day campaign at “Pont del Petroli”, Barcelona, by clustered statistical analysis. The auto-regressive wind model and the uniform circular motion phase model permitted the filter to overcome divergence in all wind and motion scenarios. The statistical indicators comparing both instruments showed overall improvement. The mean deviation increased from 1.62% (without motion correction) to −0.07% (with motion correction), while the root-mean-square error decreased from 1.87% to 0.58%, and the determination coefficient (R2) improved from 0.90 to 0.96.

Impact of floating turbine motion on nacelle lidar turbulence measurements

“…The first location (76.95 m) is chosen so that the position of the beams (50 m upstream the lidar) and their probe volume (z R = 24.75 m) are within the turbulence box, which is defined here to start at x = 2 m. For each of these i = 450 locations, we determine the position of a virtual ideal anemometer as [x, y, z] = x l (i) + n (1,1) f d , 0, z l0 , where z l0 = 64 m, f d = 51.95 m is the beam focused distance at a range 50 m, and n from Eqn. (1). The Wind Iris beams are then measuring at positions…”

“…Offshore measurements of mean winds and turbulence at modern turbine operating heights are rare and expensive. This is the reason why vertical profiling floating lidars are nowadays the standard for offshore wind energy measurements [1]. However, turbulence measurements from floating lidars are strongly impacted by the motion of the buoys in which they are deployed on [2,3,4,5].…”

A Machine Learning Approach to Correct Turbulence Intensity measured by Floating Lidars

“…The wave-induced motion problem has been extensively addressed with specialized motion-correction algorithms designed to work at different frequency regimes. Salcedo-Bosch et al [9,10] proposed a solution around the 1-Hz region which relies on an Unscented Kalman filter prototype to provide motion compensation in the absence of LiDAR high-frequency line-of-sight measurements. In contrast, [11,12,13], have tackled the issue at higher frequency rates, 50Hz.…”

Uncertainty and bias on velocities determined from an arc-scanning lidar

“…Accurate determination of wind speed offshore is important for the progression of offshore wind energy. Floating Doppler lidars offer precise measurements of the wind [1,2], but sometimes it is more convenient to place the lidar on a fixed footing, such as a transition piece of a fixed-bottom wind turbine, or on the coast. In these cases, long-ranging scanning lidars operating in an arcscanning mode offer both wind speed and direction measurements.…”