Application of simulated lidar scanning patterns to constrained Gaussian turbulence fields for load validation

Dimitrov, Nikolay Krasimirov; Natarajan, Anand

doi:10.1002/we.1992

Cited by 29 publications

(38 citation statements)

References 17 publications

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“…Any serious gaps in the data will raise the uncertainty and therefore the risk associated with an oncoming gust, which can be included in the control strategy. In addition, since the method can handle any set of scattered velocity measurements, it should be compatible with any scanning pattern the lidar might have (e.g., see [26]). …”

Section: Discussionmentioning

confidence: 99%

Assessing the Severity of Wind Gusts with Lidar

2016

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Abstract:Lidars have gained a lot of popularity in the field of wind energy, partly because of their potential to be used for wind turbine control. By scanning the oncoming wind field, any threats such as gusts can be detected early and high loads can be avoided by taking preventive actions. Unfortunately, lidars suffer from some inherent weaknesses that hinder measuring gusts; e.g., the averaging of high-frequency fluctuations and only measuring along the line of sight). This paper proposes a method to construct a useful signal from a lidar by fitting a homogeneous Gaussian velocity field to a set of scattered measurements. The output signal, an along-wind force, acts as a measure for the damaging potential of an oncoming gust and is shown to agree with the rotor-effective wind speed (a similar control input, but derived directly from the wind turbine's shaft torque). Low data availability and the disadvantage of not knowing the velocity between the lidar beams is translated into uncertainty and integrated in the output signal. This allows a designer to establish a control strategy based on risk, with the ultimate goal to reduce the extreme loads during operation.

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Section: Discussionmentioning

confidence: 99%

Assessing the Severity of Wind Gusts with Lidar

2016

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“…Thus, if we plan to use lidar measurements as inputs for load simulations, we need to preprocess the lidar wind speed observations. This process is associated with some uncertainties and difficulties as discussed in Dimitrov and Natarajan . For the case of a nacelle‐mounted, forward‐looking lidar, the main aspects to be considered are as follows: Effect of volume averaging: Due to the significant measurement volume, the velocity estimate represents the weighted sum of a distribution of velocity measurements along the line‐of‐sight (LOS) instead of a single measurement.…”

Section: Problem Formulationmentioning

confidence: 99%

Wind turbine load validation using lidar‐based wind retrievals

et al. 2019

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We define and demonstrate a procedure for carrying out wind turbine load validation based on measurements from nacelle‐mounted scanning lidars. Two coherent Doppler lidar systems, a pulsed lidar and a continuous‐wave lidar, are mounted on a 2.3‐MW wind turbine equipped with load measurement sensors. Wind measurements from a meteorological mast mounted at 2.5 rotor diameters distance are used as reference. The study shows how lidar measurements are processed and applied as inputs to aeroelastic load simulations, and the results are then compared with simulations where the wind inputs have been determined using the meteorological mast data in compliance with the IEC61400‐13 standard. For the majority of simulation cases considered, the use of nacelle‐mounted lidar measurements results in load estimation uncertainties lower or equal to those that are based on measurements from cup anemometers on the mast. These results demonstrate the usefulness of nacelle‐mounted lidars as tools for carrying out load validation without the need of meteorological masts.

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“…One of such is power-performance measurements; FL nacelle lidars decrease the statistical uncertainty of the measured power curve when compared to that based on mast measurements . The statistical uncertainty associated with load validation can potentially also be reduced (Dimitrov and Natarajan, 2016). Another important use of FL nacelle lidars is turbine control; they have the potential to reduce loads and increase energy capture Schlipf et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Turbulence characterization from a forward-looking nacelle lidar

2017

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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.

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Application of simulated lidar scanning patterns to constrained Gaussian turbulence fields for load validation

Cited by 29 publications

References 17 publications

Assessing the Severity of Wind Gusts with Lidar

Assessing the Severity of Wind Gusts with Lidar

Wind turbine load validation using lidar‐based wind retrievals

Turbulence characterization from a forward-looking nacelle lidar

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