Turbulence characterization from a forward-looking nacelle lidar

Abstract

Abstract. We present two methods to characterize turbulence in the turbine inflow using radial velocity measurements from nacelle-mounted lidars. The first uses a model of the three-dimensional spectral velocity tensor combined with a model of the spatial radial velocity averaging of the lidars, and the second uses the ensembleaveraged Doppler radial velocity spectrum. With the former, filtered turbulence estimates can be predicted, whereas the latter model-free method allows us to estimate unfiltered turbulence measures. Two types of forwardlooking nacelle lidars are investigated: a pulsed system that uses a five-beam configuration and a continuouswave system that scans conically. For both types of lidars, we show how the radial velocity spectra of the lidar beams are influenced by turbulence characteristics, and how to extract the velocity-tensor parameters that are useful to predict the loads on a turbine. We also show how the velocity-component variances and co-variances can be estimated from the radial-velocity unfiltered variances of the lidar beams. We demonstrate the methods using measurements from an experiment conducted at the Nørrekaer Enge wind farm in northern Denmark, where both types of lidars were installed on the nacelle of a wind turbine. Comparison of the lidar-based along-wind unfiltered variances with those from a cup anemometer installed on a meteorological mast close to the turbine shows a bias of just 2 %. The ratios of the unfiltered and filtered radial velocity variances of the lidar beams to the cup-anemometer variances are well predicted by the spectral model. However, other lidar-derived estimates of velocity-component variances and co-variances do not agree with those from a sonic anemometer on the mast, which we mostly attribute to the small cone angle of the lidar. The velocity-tensor parameters derived from sonic-anemometer velocity spectra and those derived from lidar radial velocity spectra agree well under both near-neutral atmospheric stability and high wind-speed conditions, with differences increasing with decreasing wind speed and increasing stability. We also partly attribute these differences to the lidar beam configuration.

How does turbulence change approaching a rotor?

“…That gives the value of a as a function of U ∞ , which we assume is equal to the velocity measured at the furthest range gate. The induction factors from the undisturbed sector (see Peña et al, 2017) are shown in Fig. 3b together with a smooth curve through the points, which is later used to compute ∂a/∂U ∞ .…”

“…The lidar measured the line-of-sight velocity at 10 range gates centered at 49,72,95,109,121,142,165,188,235, and 281 m upstream of the rotor at a sampling frequency of 0.2 Hz. All details about the experiment may be found in Peña et al (2017).…”

“…The three parameters governing the Mann spectral tensor model are selected according to the findings of Peña et al (2017) and represent the best fit to the measured conditions at Nørrekaer Enge, which is the site of the lidar turbulence measurements. The output of the Mann simulation algorithm (Mann, 1998) is a spatial box of turbulent fluctuations, which are converted to time domain via Taylor's frozen turbulence hypothesis.…”

How does turbulence change approaching a rotor?

“…The lidar measured the line-of-sight velocity at ten range gates centered at 49, 72, 95, 109, 121, 142, 165, 188, 235, and 281 m 5 upstream of the rotor at a sampling frequency of 0.2 Hz. All details about the experiment may be found in Peña et al (2017).…”

Inflow characterization using measurements from the SpinnerLidar: the ScanFlow experiment

“…Forward-looking nacelle-mounted lidars have already been proven to be useful for power performance assessments [1], load validation [2], turbine control [3], and inflow turbulence characterization [4]. This is partly because their beams probe into the wind as the lidars are fixed on the turbine's nacelle.…”