A Case Study of Laser Wind Sensor Performance Validation by Comparison to an Existing Gage
Abstract
A case study concerning validation of wind speed measurements made by a laser wind sensor mounted on a 190 square foot floating platform in Muskegon Lake through comparison with measurements made by pre-existing cup anemometers mounted on a met tower on the shore line is presented. The comparison strategy is to examine the difference in measurements over time using the paired-t statistical method to identify intervals when the measurements were equivalent and to provide explanatory information for the intervals when the measurements were not equivalent. The data was partitioned into three sets: not windy (average wind speed measured by the cup anemometers ≤ 6.7m/s) windy but no enhanced turbulence (average wind speed measured by the cup anemometers > 6.7m/s), and windy with enhanced turbulence associated with storm periods. For the not windy data set, the difference in the average wind speeds was equal in absolute value to the precision of the gages and not statistically significant. Similar results were obtained for the windy with no enhanced turbulence data set and the average difference was not statistically significant (a=0.01). The windy with enhanced turbulence data set showed significant differences between the buoy mounted laser wind sensor and the on-shore mast mounted cup anemometers. The sign of the average difference depended on the direction of the winds. Overall, validation evidence is obtained in the absence of enhanced turbulence. In addition, differences in wind speed during enhanced turbulence were isolated in time, studied and explained.
Keywords
Full Text:
PDFReferences
S. G. Jamdade and P. G. Jamdade, “Analysis of wind speed data for four locations in Ireland based on Weibull distribution’s linear regression modelâ€, International Journal of Renewable Energy Research, Vol. 2, No. 3, 2012, http://www.ijrer.org/ijrer/index.php/ijrer/article/view/258/pdf, accessed 4 February 2015.
A. M. Law, Simulation Modeling & Analysis, 4th ed., New York: McGraw-Hill, 2007.
R. G. Sargent, “Verification and validation of simulation modelsâ€, Journal of Simulation, doi:10.1057/jos.2012.20, Vol. 7, pp. 12-14, 2012.
S. McMahon and S. Watson, “A comparison of the correlation between onshore and offshore wind speed dataâ€, European Wind Energy Conference 2013, Vienna, 2013. http://proceedings.ewea.org/annual2013/allfiles2/1172_EWEA2013presentation.pdf, Accessed 3 February 2015.
C. Wissemann, “NJ offshore wind park,†IOOS and Offshore Wind Power, Rutgers University, 2009. http://rucool.marine.rutgers.edu/index2.php?option=com_content&task=view&id=157&pop=1&page=0&Itemid=28, Accessed 3 February 2015.
W. Musial, and B. Ram, Large-scale offshore wind power in the United States: Assessment of opportunities and barriers, National Renewable Energy Laboratory Technical Report. NREL/TP-500-40745, 2010.
C. Hasager, A. Peña, M. Christiansen, P. Astrup, M. Nielsen, F. Monaldo, D. Thompson, and P. Nielsen, “Remote sensing observation used in offshore wind energyâ€, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 1, No. 1, pp. 67-79, 2008.
D. Kindler, M. Courtney, and A. Oldroyd, “Testing and calibration of various LiDAR remote sensing devices for a 2 year offshore wind measurement campaignâ€, European Wind Energy Conference 2009, Marseille, http://www.enecafe.com/interdomain/idfukyo/lidar/paper/2009/Norsewind_LIDARtests.pdf, Accessed 3 February 2015.
C. Hasager, M. Badger, A. Peña, J. Badger, I. Antoniou, M. Nielsen, P. Astrup, M. Courtney, and T. Mikkelsen, Advances in offshore wind resource estimation, in Advances in Wind Energy Conversion Technology, S. Matthew and G. Philip, eds., New York: Springer-Verlag, 2011, pp. 85-106.
A. Peña, C. Hasager, S. Gryning, M. Courtney, I. Antoniou, and T. Mikkelsen, “Offshore wind profiling using light detection and ranging measurementsâ€, Wind Energy, Vol. 12, pp. 105-124. 2009.
A. Westerhellweg, B. Canadillas, A. Beeken, and T. Neumann, “One year of LiDAR measurements at FINO1-Platform: Comparison and verification to met-mast dataâ€, 10th German Wind Energy Conference, Bremen, 18-19 November 2010.
J-M Thevenoud, M. Boquet, L. Thobois, and S. Davoust, “Lidars for offshore applicationsâ€, European Wind Energy Conference 2012, Copenhagen, 16-19 March 2012, http://www.leosphere.com/wp-content/uploads/2014/03/Offshore_Leosphere_Lidars-for-offshore-applications_EWEA2012.pdf, Access 4 February 2015.
Author, “Path toward bankability of floating lidar data,†EWEA Offshore Conference, Frankfort, 19-21 November 2013.
Carbon Trust, Carbon Trust Offshore Wind Accelerator roadmap for the commercial acceptance of floating LIDAR technology, 2013. http://www.carbontrust.com/resources/reports/technology/owa-roadmap-for-commercial-acceptance-of-floating-lidar-technologies, accessed 23 Sept 2014.
Author, WindSentinal floating lidar validation part 1: accuracy, http://www.youtube.com/watch?v=njxoQhZzJ50&feature=youtu.be. Accessed 5 January 2014.
Y. Pichugina, Y, R. Banta, W. Brewer, S. Sandberg, and R. I. Hardesty, “Doppler lidar–based wind-profile measurement system for offshore wind-energy and other marine boundary layer applicationsâ€, Journal of Applied Meteorology and Climatology, Vol. 51, pp. 327-249, 2012.
AXYS Technologies Inc., WindSentinel: field test data summary, 2010.
D. M. Jaynes, “Investigating the efficacy of floating lidar motion compensation algorithms for offshore wind resource assessment applicationsâ€, European Wind Energy Conference 2011, Brussels, 14-17 March 2011, http://axystechnologies.com/wp-content/uploads/2013/12/Investigating-the-Efficacy-of-Floating-LIDAR-Motion-Compensation.pdf, accessed 4 February 2015.
DOI (PDF): https://doi.org/10.20508/ijrer.v5i2.2167.g6607
Refbacks
- There are currently no refbacks.
Online ISSN: 1309-0127
Publisher: Gazi University
IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);
IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.
WEB of SCIENCE in 2024;
h=33,
Average citation per item=6.17
Impact Factor=(1749+1867+1731)/(201+146+171)=10.32
Category Quartile:Q4