Analysis of inflow parameters using LiDARs

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10th _{PhD Seminar on Wind Energy in Europe}

28-31 October 2014, Orleans, France

Analysis of inflow parameters using LiDARs

A. Giyanani1, W.A.A.M. Bierbooms2, G.J.W. van Bussel3

1_{Delft University of Technology, Wind Energy Research Group, Faculty of Aerospace}

Engineering, , Kluyverweg 1, 2629HS Delft, The Netherlands,a.giyanani@tudelft.nl

2_{Delft University of Technology,}_{w.a.a.m.bierbooms@tudelft.nl}

ABSTRACT

Remote sensing of the atmospheric variables with the use of LiDAR is a relatively new technique for wind resource assessment and oncoming wind prediction in wind energy. The validation of LiDAR measurements and comparisons with other sensing elements thus, is of high importance for further applications of the data. A measurement campaign with two vertical scanning pulsed LiDARs and met mast measurements was used here for comparison of inflow wind variables from LiDAR, sonic and cup anemometers. A comparison of the wind directions, wind speed and wind shear was performed to determine the validity of LiDAR measurements in wind energy applications. The LiDAR measurements correlated well with met mast measurements and a major cause for wind direction bias was found.

INTRODUCTION

LiDAR is an acronym for light detection and ranging. LiDARs are laser based systems working on principles similar to that of Radars or SODARs. In case of LiDAR, a light pulse is emitted in the atmosphere. The light beam is scattered in all directions from molecules and particles in the atmosphere. Some portion of this light is scattered back in the direction of the LiDAR system, which is then collected by a telescope and focussed upon a photodetector that measures the amount of backscattered light as a function of distance from the LiDAR.

The LiDAR technology is continuously upgrading to provide accurate measurements and information about the atmospheric processes much higher than the present masts. By correctly analysing the wind data coming towards the wind turbine, the wind evolution towards the blade could be realised using a prediction model. Such a model when incorporated in the control system of the wind turbine can be used to adapt the wind turbine to the incoming wind conditions in real time and hence achieve load reductions and higher energy output [1]. In order to successfully develop a wind model, a study aimed at validation of the LiDAR measurements with other measurement sensors was performed. The met mast measure-ments from sonic and cup anemometer are compared for wind directions, wind speed correlations and shear ratio comparisons.

Within the LAWINE (Lidar Applications for Wind farm Efficiency) project initiated by ECN, The Nether-lands in cooperation with XEMC Darwind, AventLidar Technology and TU Delft under the framework of Top consortium for Knowledge and Innovation Offshore Wind (TKI-WoZ), two measurement cam-paigns are carried out to evaluate the applications of LiDAR in wind energy.The project lays emphasis on testing and developing the LiDAR technology, wind resource and power performance assessment, optimisation of wind turbine control, load reduction and optimisation of wind farm operation [2].

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(a) EWTW test site with MM3 (b) Boom heights and sensors Figure 1: Site details of the EWTW test site

The meteorological mast 3 (MM3) at the test site (EWTW) in the Wieringemeer was considered for the study, see fig. 1(a). The test site is characterised by flat terrain, consisting of mainly agricultural farms with individual spread out farmhouses and rows of trees. The lake IJsselmeer is located at the distance of 2 km East of MM3. Around the MM3, several wind turbines are operating, a row of 5 Nordex N80 turbines in the North, the prototype turbines and a NEG Micon NM52 turbine in the South [3].

The MM3 is a lattice tower with guy wires fixed at a radius of 60m from the tower base at 60◦, 180◦and 300◦with respect to the North. The booms are constructed in three directions namely 0◦, 120◦and 240◦ following the IEC and Measnet guideleines as shown in fig. 1(b). The sonic anemometer is installed at 0◦ boom at three heights 52m, 80m and 108m, the cup anemometers are installed on the 120◦and 240◦ respectively at heights 52m and 80m, while two WindCube LiDAR systems are located at the foot of one of the guy wires i.e. 180◦. The wind vanes are installed on the booms at 120◦ and 240◦ at 51.2m and 79.2m. We consider them to be the same height as the cup and sonic for this study.

COMPARISON OF LIDAR WITH OTHER SENSORS

The LiDARs at the EWTW test site are used for comparison against the metmast sensors at three heights basically namely 52m, 80m and 108m heights. The data consists of 10 min averages for 30 weeks, from 1stJuly 2013 to 26thJanuary 2014 and 30240 data points in total. The cup anemometers at 120◦and 240◦ are averaged to reduce the tower shadowing effects. Data filtering for stuck sensors, low CNR < -17 dB, availability lower than 75%, wake and tower shadows and other common filters have been applied to the respective data. 01/07 01/08 01/09 01/10 01/11 01/12 01/01 01/02 10 20 30 40 50 Time steps

Wd Diff (Lidar − Son)

WC2@108m−Son@108m

Figure 2: Effect of metal guy wires on LiDAR wind direction measurements

The comparison of wind direction between two sensors are intended to find general errors in data and to detect the wake and tower shadow sectors which can be then eliminated for the power calculations. The results for comparison of windroses between LiDAR and cup anemometer after filtering and removing error records is shown in fig. 3. A bias was observed in one of the LiDAR wind direction values which changed further once during the measurement as seen in fig. 2. The reason for such drastic change

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5% 10% 15% WEST EAST SOUTH NORTH 0 − 3 3 − 6 6 − 9 9 − 12 12 − 15 15 − 18 18 − 21 21 − 24 24 − 27

(a) Windrose vane at 80m height after filtering

(b) Windrose LiDAR at 80m height after bias correction Figure 3: Comparison of Windroses at 80m height

(a) Windrose sonic at 108m height after filtering

(b) Windrose LiDAR at 108m height after bias correction Figure 4: Comparison of Windroses at 108m height

was associated with the presence of guy wires in the vicinity and error in recalibration of the LiDAR compass. The LiDAR wind directions after bias correction correlate well with the cup anemometer wind directions. A similar correlation can be seen in the comparison with sonic anemometer at 108m height in fig. 4.

The linear regression analysis of wind speeds from LiDAR, cup and sonic anemometers are compared to check the performance of the LiDARs. The filtering due to tower and wake effects reduce the data substantially. However, as can be seen from fig. 5, the LiDAR wind speed data correlates well with wind speeds from cup and sonic anemometers.

The wind shear was compared using the wind shear ratio i.e. ratio of wind speed at higher height against the wind speed at a lower height from the same sensor. The wind shear ratio was then expressed as Sr,

while h1 and h2 are the maximum and minimum measurement heights respectively, see eq. (1). The cor-relation between the shear ratio from LiDAR data and cup and sonic anemometer correlated moderately well. LiDAR data correlates better with the cup anemometers (0.8) than the sonic anemometers (0.7). Considering that the LiDAR performs volumetric averaging, it was assumed that this averaging reduced the correlation.

Sr =

Uh1

U_h2 (1)

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0 5 10 15 20 25 0 5 10 15 20 25 y = (1.0082)*x + (0.013771) R2 = (0.99436) N = 19399 Ws Cup@80m Ws WC2@80m data points linear fit

(a) Linear regression with LiDAR and cup anemometer

0 5 10 15 20 25 30 0 5 10 15 20 25 y = (0.99221)*x + (0.028172) R2 = (0.99685) N = 15805 Ws Son@108m Ws WC2@108m data points linear fit

(b) Linear regression with LiDAR and sonic aneomometer Figure 5: Linear Regression of Lidars with other sensors

0 0.5 1 1.5 2 2.5 3 3.5 4 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 y = (0.87608)*x + (0.14474) R2 = (0.79545)

N = 26682 no. of data points after filtering

SrCupf or U80/U52 S rW C 2 f or U8 0 / U5 2 data points linear fit

(a) LiDAR versus cup anemometer

0 1 2 3 4 5 6 0 1 2 3 4 5 6 7 y = (0.72596)*x + (0.31711) R2 = (0.70251)

N = 14778 no. of data points after filtering

SrSonf or U108/U52 S rW C 2 f or U1 0 8 / U5 2 data points linear fit

(b) LiDAR versus sonic aneomometer Figure 6: Linear Regression for Shear ratios of LiDAR with other sensors

CONCLUSIONS

The LiDAR’s wind direction and the wind speed measured at different heights correlated well with the other measurement sensors like the cup and sonic anemometer. The shear ratios measured at site correlated moderately well. Hence, the pulsed LiDARs prove to be a reliable measurement system and are optimal for developing wind evolution models by predicting the wind speed in flat terrain. At the EWTW test site, the wind direction self calibration was affected by presence of huge metal installations near the LiDAR. Hence, it is necessary to avoid the auto-correction of the wind directions by offsets in such cases.

REFERENCES

[1] F. Dunne and E. Simley, “LIDAR Wind Speed Measurement Analysis and Feed-Forward Blade Pitch Control for Load Mitigation in Wind Turbines,” no. October, 2011.

[2] J. W. Wagenaar, G. Bergman, and K. Boorsma, “Measurement plan LAWINE project tasks A and C.” 2013.

[3] P. Eecen and J. P. Verhoef, “EWTW Meteorological database.” 2007.