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Wind Farm Power Performance Verification Using Non-Parametric Statistical Inference

Authors: M. Celeska, K. Najdenkoski, V. Dimchev, V. Stoilkov


Accurate determination of wind turbine performance is necessary for economic operation of a wind farm. At present, the procedure to carry out the power performance verification of wind turbines is based on a standard of the International Electrotechnical Commission (IEC). In this paper, nonparametric statistical inference is applied to designing a simple, inexpensive method of verifying the power performance of a wind turbine. A statistical test is explained, examined, and the adequacy is tested over real data. The methods use the information that is collected by the SCADA system (Supervisory Control and Data Acquisition) from the sensors embedded in the wind turbines in order to carry out the power performance verification of a wind farm. The study has used data on the monthly output of wind farm in the Republic of Macedonia, and the time measuring interval was from January 1, 2016, to December 31, 2016. At the end, it is concluded whether the power performance of a wind turbine differed significantly from what would be expected. The results of the implementation of the proposed methods showed that the power performance of the specific wind farm under assessment was acceptable.

Keywords: Canonical correlation analysis, power curve, power performance, wind energy.

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[1] IEC 61400. Part 12–1: Power Performance Measurements of Electricity Producing Wind Turbines; IEC 61400-12-1, International Standard; 2005.
[2] M. Lydiaa, S. S. Kumarb, A. I. Selvakumara, G. E. Prem Kumar, “A comprehensive review on wind turbine power curvemodeling techniques”, ELSEVIER Renewable and Sustainable Energy vol. 30, pp. 452–460, 2014.
[3] A. Llombart, S. J. Watson, D. Llombart, J. M. Fandos, “Power Curve Characterization I: Improving the Bin Method”, Proceedings of International Conference on Renewable Energies and Power Quality, Zaragoza, Spain, March 2005.
[4] W. Hernandez, J. L. López-Presa, J. L. Maldonado-Correa, “Power Performance Verification of a Wind Farm Using the Friedman’s Test”, Sensors 2016, vol. 16, 816; doi:10.3390/s16060816.
[5] W. Hernandez, J. L. Maldonado-Correa, “Power Performance Verification of a Wind Turbine by using the Wilcoxon Signed-Rank Test”, IEEE Transactions on Energy Conversion, vol. 32, no. 1, March 2017.
[6] A. Albers, H. Klung, D. Westermann, “Power Performance Verification”, Proceedings of European Wind Energy Conference, 1-5 March, 1999, Nice, France, pp. 657¬–660.
[7] H. Oh, B. Kim, “Comparison and verification of the deviation between guaranteed and measured wind turbine power performance in complex terrain”, Energy Journal, vol. 85, 2015, pp. 23-29.
[8] F. Pedersen, S. Gjerding, P. Enevoldsen, J.K. Hansen, H.K. Jørgensen, “Wind turbine power performance verification in complex terrain and wind farms”, Denmark. Forskningscenter Risoe. Risoe-R; No.1330, 2002.
[9] M. Lee, S. Hur, N. Choi, “A numerical simulation of flow field in a wind farm on complex terrain”, Proceedings of the Seventh Asia-Pacific Conference on Wind Engineering, Taipei, Taiwan, 8–12 November 2009; pp. 1–8.
[10] G. Polanco, V. M. Shakeel, “Role of advanced CAE tools in the optimization of wind resource assessment of complex terrains”, Proceedings of the 4th IEEE International Conference on Cognitive Info communications, Budapest, Hungary, 2–5 December 2013; pp. 687–691.
[11] C. Xu, J. Yang, C. Li, W. Shen, Y Zheng, D Liu, “A research on wind farm micro-sitting optimization in complex terrain”, Proceedings of the 2013 International Conference on Aerodynamics of Offshore Wind Energy Systems and Wakes, Lyngby, Denmark, 17–19 June 2013; pp. 669–679.
[12] Brian S. Everitt, David Howell, “Encyclopedia of Statistics in Behavioral Science”, ISBN: 978-0-470-86080-9, Hoboken, New Jersey, John Wiley & Sons, April 2005.
[13] X. Ye, Z. Lu, Y. Qiao, Y. Min, M. O'Malley, “Identification and Correction of Outliers in Wind Farm Time Series Power Data”, IEEE Transactions on power systems, vol. 31, no. 6, November 2016, pp. 4197 4205.
[14] Y. Wan, M. Milligan, and B. Parsons, “Output power correlation between adjacent wind power plants,” J. Sol. Energy Eng., vol. 125, no. 4, November 2003, pp. 551 555.
[15] H. Pham, Handbook of Engineering Statistics, Springer-Verlang, London, 2006, pp. 117-118.
[16] Weisstein, Eric W. "Bivariate Normal Distribution." From MathWorld--A Wolfram Web Resource, last visited 07.11.2017 at 11:21-hour
[17] J. E. Borovsky, “Canonical correlation analysis of the combined solar wind and geomagnetic index data sets”, Journal of Geophysical Research: Space Physics, vol. 119, July 2014, pp. 5364–5381.
[18] J. H. Steiger, A. R. Hakstian, “The asymptotic distribution of elements of a correlation matrix: Theory and application”, British Journal of Mathematical and Statistical Psychology, vol. 35, Issue 2, November 1982, pp. 208–215.