WIND ENERGY

Wind Energ. 2014; 17:413433

Published online 7 February 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/we.1584

RESEARCH ARTICLE

Analysis of light detection and ranging wind speedmeasurements for wind turbine controlEric Simley1, Lucy Y. Pao1, Rod Frehlich2, Bonnie Jonkman3 and Neil Kelley3

1 Dept. of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, Colorado, USA2 Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, Colorado, USA3 National Renewable Energy Laboratory (NREL), Golden, Colorado, USA

ABSTRACT

Light detection and ranging (LIDAR) systems are able to measure the speed of incoming wind before it reaches a windturbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbinestructural loads. However, the degree to which such preview-based control techniques can reduce loads by reacting toturbulence depends on how accurately the incoming wind field can be measured. This study examines the accuracy ofdifferent measurement scenarios that rely on coherent continuous-wave or pulsed Doppler LIDAR systems, in terms ofroot-mean-square measurement error, to determine their applicability to feedforward control. In particular, the impactsof measurement range, angular offset of the LIDAR beam from the wind direction, and measurement noise are studiedfor various wind conditions. A realistic simulation case involving a scanning LIDAR unit mounted in the spinner of aMW-scale wind turbine is studied in depth, with emphasis on preview distances that provide minimum measurement errorfor a specific scan radius. Measurement error is analyzed for LIDAR-based estimates of point wind speeds at the rotor aswell as spanwise averaged blade effective wind speeds. The impact of turbulence structures with high coherent turbulentkinetic energy on measurement error is discussed as well. Copyright 2013 John Wiley & Sons, Ltd.

KEYWORDS

LIDAR; wind turbine control; feedforward control

Correspondence

L. Y. Pao, University of Colorado, Engineering Center, 425 UCB, Boulder, Colorado 80309, USA.E-mail: pao@colorado.edu

Received 6 September 2011; Revised 13 November 2012; Accepted 23 November 2012

NOMENCLATURE

CTKE coherent turbulent kinetic energyCW continuous-waveGP_LLJ Great Plains-low level jetLIDAR light detection and rangingRMS root-mean-squareRPM rotations per minuteTKE turbulent kinetic energyU ; V mean wind speeds in the x and y directionsd measurement preview distancevr radial wind velocityF LIDAR focus distanceR range along LIDAR beamW .F ;R/ LIDAR range weighting function laser wavelengtha2 e

2 intensity radius of laser beamr full width at half maximum pulse widthp range gate size

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u; v; w time-varying wind speeds in the x; y; z directions LIDAR measurement angle off of longitudinal (x) direction LIDAR measurement azimuth angle in the rotor (yz) planeOu estimate of u componentr radius of circular scan pattern random measurement noise standard deviation of random measurement noiseflidar rotational rate of LIDAR scanfs LIDAR sampling rateU D

average friction velocity of wind in rotor planeD wind shear power-law exponentRiTL turbine layer gradient Richardson numberu; v ; w standard deviation of wind componentsCQ.i/ coefficient of torque for blade element iAAnn.i/ area of annulus swept out by blade element iSbb.f / power spectral density of blade effective wind speedSll.f / power spectral density of LIDAR measurementSbl.f / cross-power spectral density between blade effective wind speed and LIDAR measurement

1. INTRODUCTION

Wind speed measurements upwind of a wind turbine can be used as part of feedforward or preview-based control archi-tectures to help regulate rotor speed and mitigate structural loads caused by turbulent wind conditions. Prior analyseshave shown that reductions in turbine loads and improved rotor speed regulation can be achieved with knowledge of theincoming wind field.14 A block diagram of such a control strategy is shown in Figure 1. Upstream wind is measured,providing an estimate of the wind speeds that will reach the turbine after a delay time of d=U , where d is the previewdistance between the rotor and the measurement location, and U is the mean wind speed. In reality, the turbulent structuresin the wind will evolve between the time they are measured and when they reach the turbine, causing errors in the previewwind measurements.5,6 In this paper, we focus on analyzing sources of error during the preview measurement stage,ignoring wind evolution. Thus, our analysis assumes the validity of Taylors frozen turbulence hypothesis,7 which statesthat turbulent eddies remain unchanged while advecting with the average wind velocity.

Various optical and acoustical methods exist for measuring wind speeds, including incoherent direct detection LIDARand sonic detection and ranging.8 However, coherent Doppler LIDAR is a relatively inexpensive and accurate way toprovide remote measurements at various locations in front of a turbine rotor for blade pitch control.8 The two main coherentLIDAR technologies that are currently available are CW LIDAR and pulsed LIDAR. In this paper, we provide an analysisof using both CW and pulsed LIDAR technologies to provide preview wind measurements, with an emphasis on CW.

During the SpinnerEx 2009 program, a ZephIR CW LIDAR, developed by QinetiQ and Natural Power ConsultantsLtd, mounted in the spinner of a 2.5 MW wind turbine, was successfully tested near Tjreborg, Denmark, illustrating thefeasibility of rotating measurements of the wind in front of the rotor at a given scan radius.9,10 Although some improvementcan be gained by measuring wind speeds upwind of the hub location using a fixed LIDAR direction, it is much moreadvantageous to be able to measure the wind that will arrive at each individual blade,11 and a rotating hub-mounted

Desired Set Points

Wind

Pitch Command

Regulated Outputs (speed, sway, deflection)

Figure 1. A block diagram illustrating how LIDAR is used in a preview-based combined feedforward/feedback control scenario. Thewind evolution block represents the coherence loss between wind measured by the LIDAR and the wind encountered by the rotorafter a delay time of d=U. The distance d is the distance upwind of the rotor where the measurement is taken and U is the mean

wind speed.

414 Wind Energ. 2014; 17:413433 2013 John Wiley & Sons, Ltd.DOI: 10.1002/we

E. Simley et al. Analysis of LIDAR measurements

LIDAR is well suited for this application. Therefore, we provide a detailed analysis of preview wind measurements at avariety of blade span positions using simulations of a single spinning LIDAR mounted in the hub of the National Renew-able Energy Laboratorys (NRELs) 5 MW reference wind turbine model.12 Analyses are provided using separate CW andpulsed LIDAR models developed for use with wind fields generated using NRELs TurbSim code.13,14 The CW model isbased on the ZephIR 300 LIDAR,15,16 and the pulsed model is based on the Leosphere Windcube WLS7.15,17

By simulating rotating LIDAR measurements, the influence of circular scan radius and preview distance on measurementerror can be analyzed. Optimal scan radii and preview distances that produce minimum RMS measurement error are foundfor different wind conditions to understand the trade-offs that must be made when designing a scanning LIDAR system forfeedforward control. The error in the estimation of the u component, perpendicular to the rotor plane, is analyzed becausethis is the component that has the greatest impact on turbine loads. Detection of transverse wind speeds is treated as anerror source. First, the dependence of RMS error on scan geometry is studied for estimation of the u component at theLIDAR focus point in order to find optimal preview distances for various scan radii. Next, the optimal scan geometry isfound for the different wind conditions for estimation of the spanwise averaged blade effective wind speed, which is theuniform wind speed that would cause the same response from a blade as the existing spatial distribution of wind speeds.The blade effective wind speed is the variable of interest for a control system that uses blade pitch to react to previewLIDAR measurements.

The paper is organized as follows. LIDAR measurement theory is presented in Sections 24. In Section 2, we discussthe principal equation used in our LIDAR measurement model, called the range weighting function, which describeshow wind speeds are spatially averaged during a measurement. Section 3 provides an explanation of how transversewind components cause errors in the estimate of the longitudinal wind component of interest, through a process we calldirectional bias. An introduction to random measurement noise inherent in LIDAR measurements is provided in Section 4.The rest of the paper discusses results from simulated LIDAR measurements of wind fields generated by TurbSim. Adescription of the wind fields used for simulation, characteristic of above-rated wind conditions in the U.S. Great Plainsregion, is included in Section 5. Section 6 contains results from simulations of a hub-mounted scanning LIDAR scenario,illustrating typical preview distances d that provide the minimum RMS measurement error, with and without measurementnoise, for different scan radii. Section 7 contains analyses of RMS error between the LIDAR measurement and the bladeeffective wind speed for different scan geometries. The impact of coherent structures, typical during stable boundary layers,on measurement error is shown in Section 8. Finally, Section 9 concludes the paper with a discussion of the results andnecessary future work.

The LIDAR measurement theory in Sections 2 and 3 is an improved explanation of work published previously inSimley et al.18 Work similar to the optimal scan geometry results in Section 6 was also provided in Simley et al.;18 however,additional analyses of measurement noise and pulsed LIDAR measurements are provided in this paper. In addition,Section 7 on blade effective wind speed measurements is an important analysis for blade pitch control that was not discussedin previous work. Section 8 on the impact of coherent structures is also a new study.

2. LIDAR RANGE WEIGHTING

Measurements from both CW and pulsed LIDAR contain wind speed contributions from a volume in space rather thanonly the point where the LIDAR measurement is intended. Since the transverse dimension of the laser beam is very narrowcompared with the along-range length of the sampling volume, the sampling volume is modeled as an infinitely thin beam.Wind speeds are integrated along the laser beam according to a range weighting function W .F ;R/ to form the detectedradial velocity, where R is the range along the beam and F is the distance along the beam where the measurement isintended. The wind speed measurement due to range weighting of the line-of-sight velocity at a measurement distance Fis determined by

vlos.F /DZ 10

vr .R/W .F ;R/dR (1)

where vr .R/ is the radial wind velocity (wind velocity projected onto the LIDAR beam direction) at a range R along thelaser beam.19 An example of a range weighting function is illustrated using the light gray curve above the LIDAR beam inFigure 3 in the next section.

2.1. Continuous-wave range weighting

Continuous-wave LIDAR determines the radial wind speed at a specific location by focusing the laser beam at that location.However, rather than only detecting the wind speed at the focus point, wind speed values along the laser beam are integrated

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according to a range weighting function inherent in the physics of the LIDAR system. When the effects of refractiveturbulence on laser propagation are ignored, the CW range weighting function19 for a focus distance F is given by

W .F ;R/D8