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Delegates are invited to meet and discuss with the poster presenters during the poster presentation sessions between 10:30-11:30 and 16:00-17:00 on Thursday, 19 November 2015.

Lead Session Chair:
Stephan Barth, ForWind - Center for Wind Energy Research, Germany
Dimitri Foussekis C.R.E.S., Greece
Co-authors:
Dimitri Foussekis (1) F George Sieros (1)
(1) C.R.E.S., Pikermi, Greece

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2015 and are published here unedited

D. Foussekis received his Bachelor's of Science in Physics in Greece and his MSc and PhD in Fluid Mechanics in France. He is a senior Research Engineer at C.R.E.S. and his primary research interests lie in the fields of i) wind potential studies (member of MEASNET’s Expert Group for Site Assessment), ii) LIDAR and SODAR performance evaluation in complex terrains and iii) design and implementation of remotely controlled measurement systems for mission critical applications (wind farm monitoring, load and power performance measurements of wind turbines). He has more than 50 papers, presentations and announcements in scientific journals, conferences and workshops.


Poster

Poster Download poster (13.99 MB)

Abstract

CFD-based corrections of LiDAR measurements at several complex terrain sites

Introduction

Recent experiments have shown that in complex terrain sites, measurements of the horizontal wind speed from mast-mounted cup anemometers and ground-based LiDARs, present deviations that usually reach the range of 4-8%. These deviations are due to the different principles of measurements (point vs large volume) combined with the unavoidable wind flow inhomogeneity occurring at complex topographies. Therefore, a conversion of the results is necessary for the two measurements to be comparable. The present work describes the assessment of these deviations for several complex terrain sites, using a home-made CFD numerical tool, validated already in several round-robins and blind-test runs.

Approach

The present work presents results from several measurement campaigns, performed at various types of complex terrains, in Greece. These short-term LiDAR measurement campaigns (2-4 months), aimed to reveal the detailed wind shear and veer at the mast location. All datasets include the concurrent wind data from the top cup anemometer, mounted on an adjacent IEC-compliant (usually 40m high) meteorological mast. The numerical tool employed to assess the wind flow over the sites is "CRES-Flow", a CFD code that uses Reynolds averaged Navier-Stokes (RaNS) equations and the k-ω turbulence model.

Main body of abstract

LiDARs measure several radial wind speeds (at a cone angle of 30deg) and deduce the horizontal wind speed and direction, by assuming (instantaneous) flow-homogeneity within the measurement volume. This assumption is violated in complex flows, as confirmed by the isovelocities calculated by the CFD model for the scanned volume. In order to calculate the bias error, CRES-Flow is parameterized separately for each site, in terms of mesh size, number of cells, etc, so that the max. possible number of nodes are found into the LiDAR’s scan cone.
All case studies involve mountainous areas of 500+m ASL heights and (at least) two main wind directions. Bias results show that, as expected, are direction dependent and most of the times are reduced with height.


Conclusion

Results confirm that the variation of the vertical wind speed component, within the scan code, is the main source of deviation of the horizontal wind speed between Mast and LiDAR measurements. The proposed methodology reduces to 1-2% the observed deviations, by applying direction-dependent conversion factors to the LiDAR measurements.


Learning objectives
The methodology, if applied prior the LiDAR (or even the met. mast) installation can provide indication for the best measurement location and device orientation. Results confirm that LiDARs are suitable for resource assessment purposes and any deviations to the Mast results can be explained and converted using an adequate flow model.