Abstract

Abstract. Lidar systems have the potential of alleviating structural loads on wind turbines by providing a preview of the incoming wind field to the control system. For a collective pitch controller, the important quantity of interest is the rotor-effective wind speed (REWS). In this study, we present a model of the coherence between the REWS and its estimate from continuous-wave nacelle-mounted lidar systems. The model uses the spectral tensor definition of the Mann model. Model results were compared to field data gathered from a two- and four-beam nacelle lidar mounted on a wind turbine. The comparison shows close agreement for the coherence, and the data fit better to the proposed model than to a model based on the Kaimal turbulence model, which underestimates the coherence. Inflow conditions with larger length scales led to a higher coherence between REWS and lidar estimates than inflow turbulence of smaller length scale. When comparing the two lidar systems, it was shown that the four-beam lidar is able to resolve small turbulent structures with a higher degree of coherence. Further, the advection speed by which the turbulent structures are transported from measurement to rotor plane can be estimated by 10 min averages of the lidar estimation of REWS. The presented model can be used as a computationally efficient tool to optimize the position of the lidar focus points in order to maximize the coherence.

Highlights

  • The control system is an integral part of a wind turbine and has substantial influence on its behaviour

  • We presented a model of the coherence between rotor-effective wind speed (REWS) estimated from turbine and lidar measurements

  • It is compared to field data obtained from two continuous-wave lidar systems mounted on top of the nacelle of a wind turbine

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Summary

Introduction

The control system is an integral part of a wind turbine and has substantial influence on its behaviour. In the ideal case of perfect lidar measurements of veff and turbine modelling, disturbances can be completely rejected and optimal rotor speed control can be achieved (Dunne et al, 2011). This is not achievable in reality and important shortcomings of the lidar systems are. Periods where the lidar’s vision was obstructed by hard targets showed an increase in DELs, emphasizing the influence of environmental conditions on lidar measurements Another experiment on CART2 was performed by Kumar et al (2015), where, besides adding a feedforward controller, the gains of the feedback controller had been reduced. Cross-spectra of REWS from turbine and lidar measurements and the optimization of the lidar focus point positions

Methodology
Overview coherence model
REWS estimated from turbine measurements
REWS estimated from lidar measurements
Determination of the cross-spectrum
Model implementation and validation against simulations
Instrumentation
Site characterization
Results
Comparison of mean wind speeds
Comparison of coherence
Time delay analysis
Conclusions
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