CONSIDERING AIR DENSITY EFFECT ON MODELLING WIND FARM POWER CURVE USING SITE MEASUREMENTS

Year 2017, Volume: 5 Issue: 1, 420 - 430, 30.06.2017

Ceyhun Yildiz , Mustafa Tekin Ahmet Gani , O. Fatih Kececioglu , Hakan Acikgoz Mustafa Sekkeli

https://doi.org/10.17261/Pressacademia.2017.619

Abstract

Manufacturers
develop power curves for their wind turbines. Customers use these wind turbine
power curves for wind farm planning and estimating nearly total production of
planned plant. When wind farm is installed and connected to the grid, these
power curves are not useful. In literacy, researchers proposed wind turbine
power curve measurement methods to obtain an accurate power curve for turbine
on site. But it is not easy to develop power curves for clusters of wind
turbines. Developing a single power curve for a wind farm slightly simplifies
this problem. Accurate wind farm power curve is a very useful tool for
converting wind speed forecasts to power. Also plant owner can use this tool to
detect anomalous operations. In this study we developed and tested wind farm
power curves by using real site measurements. Two different methods are used to
develop power curves. They are polynomial curve fitting and mean bins method.
Wind speed and power output relation is investigated. A method is proposed to
add effect of air density on power curve. Developed power curve has two inputs.
They are hourly mean wind speed and air density values. This approach uses
variable air density in calculation of wind farm power output. Results of this
study showed that performance of mean bins method is better than polynomial
curve fitting. Also proposed air density effect adding method improves
performances of obtained power curves.  

Keywords

Wind farm, power curve, air density effect, mean power bins, wind turbine power curve

References

• Bitar, Eilyan Y., et al. "Bringing wind energy to market." Power Systems, IEEE Transactions on 27.3 (2012): 1225-1235.
• Evans, Annette, Vladimir Strezov, and Tim J. Evans. "Assessment of utility energy storage options for increased renewable energy penetration." Renewable and Sustainable Energy Reviews 16.6 (2012): 4141-4147.
• A. Picard, R. Davis, M. Gl¨aser, and K. Fujii, “Revised formula for thedensity of moist air (cipm-2007),” Metrologia, vol. 45, pp 149–155, 2008
• Marvuglia, Antonino, and Antonio Messineo. "Monitoring of wind farms’ power curves using machine learning techniques." Applied Energy 98 (2012): 574-583.
• Chang, Tian-Pau, et al. "Comparative analysis on power curve models of wind turbine generator in estimating capacity factor." Energy 73 (2014): 88-95.
• Lydia, M., Sahoo Subhendu Kumar, and G. E. P. Kumar. "Advanced algorithms for wind turbine power curve modeling." Sustainable Energy, IEEE Transactions on 4.3 (2013): 827-835.
• Olaofe, Zaccheus O., and Komla A. Folly. "Wind energy analysis based on turbine and developed site power curves: A case-study of Darling City." Renewable Energy 53 (2013): 306-318.
• Carrillo, C., et al. "Review of power curve modelling for wind turbines." Renewable and Sustainable Energy Reviews 21 (2013): 572-581.
• Lydia, M., et al. "A comprehensive review on wind turbine power curve modeling techniques." Renewable and Sustainable Energy Reviews 30 (2014): 452-460.
• Milan, Patrick, Matthias Wächter, and Joachim Peinke. "Stochastic modeling and performance monitoring of wind farm power production." Journal of Renewable and Sustainable Energy 6.3 (2014): 033119.
• Farkas, Zeno. "Considering air density in wind power production." arXiv preprint arXiv:1103.2198 (2011).
• WAN, Y.; ELA, Erik; ORWIG, Kirsten. Development of an equivalent wind plant power curve. In: Proc. WindPower. 2010. p. 1-20.
• Compatibility, Electromagnetic. Part 4: 30: Testing and measurement techniques–Power quality measurement methods. IEC 61000-430 Std, 2003.
• Turbines, Wind. Part 12-1: Power performance measurements of electricity producing wind turbines; IEC TC/SC 88. IEC 61400-12-1, 2005.
• Ackermann, Thomas, ed. Wind power in power systems. John Wiley & Sons, 2005.