Abstract
The power curve is the performance indicator of the wind turbine. The current industry standard warranted by the International Electrotechnical Commission (IEC) 61400-12-1 (IEC 61400-12-1: first edition 2005-12, wind turbines—part 12-1: power performance measurements of electricity producing wind turbines [1]) defines a unique procedure to evaluate a wind turbine’s power curve (for both stall and pitch regulated wind turbine). The IEC 61400-12-1 (IEC 61400-12-1: first edition 2005-12, wind turbines—part 12-1: power performance measurements of electricity producing wind turbines [1]) standard gives a leverage to estimate any incomplete bin of power curve from two adjacent complete bins using linear interpolation method whenever the incomplete bin prevents the completion of test due to data insufficiency. The incomplete bin interpolation approach could add additional uncertainty in the power performance assessment due to the nonlinear power performance characteristics of wind turbines. Especially in stall regulated wind turbine, the approach over estimates the power in incomplete bin about 32% and leading to 1.2% over estimation of annual energy production (AEP). This paper proposes to use scaled down time averaging window data sets instead of traditional interpolation method to evaluate the incomplete bins of IEC power curve by employing the statically strong data points. In this study, thirty second average data sets have been used to evaluate the incomplete bin. The technique has been implemented in both stall and pitch regulated wind turbines power curve data bases and found that the results are reasonable and realistic. The proposed technique improves the power estimation by 30.4% and the AEP by 1.1% in a stall regulated wind turbine when compare to interpolation method. The paper strives to demonstrate that the incomplete bins of IEC database can be filled with the data sets typically averaged over thirty seconds to derive actual IEC power curve.
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