Abstract
Power curves provided by wind turbine manufacturers are obtained under certain conditions that are different from those of real life operation and, therefore, they actually do not describe the behavior of these machines in wind farms. In those cases where one year of data is available, a logistic function may be fitted and used as an accurate model for such curves, with the advantage that it describes the power curve by means of a very simple mathematical expression. Building such a curve from data can be achieved by different methods, such as using mean values or, alternatively, all the possible values for given intervals. However, when using the mean values, some information is missing and when using all the values the model obtained can be wrong. In this paper, some methods are proposed and applied to real data for comparison purposes. Among them, the one that combines data clustering and simulation is recommended in order to avoid some errors made by the other methods. Besides, a data filtering recommendation and two different assessment procedures for the error provided by the model are proposed.
Highlights
Wind turbine performance is usually described by means of the relationship between output power and wind speed
In order to1.define the bestbetween methoddata to assess the after application toFiltered the wind turbine power curve, firstly the reference one is defined, based on the spline, by using the same procedure as Proposed the proposed by IEC 61400-12
The CD MAPE for the spline is over 2%
Summary
Wind turbine performance is usually described by means of the relationship between output power and wind speed. As there is a wide variability in the real data [18], and the objective is to have accurate information of the installed wind turbine, a possible option is to use the simplest model for the power curve with valid results. The simplest method to apply the model may be to define a number of intervals for the wind speed, to identify each interval by the mean value and to assign the mean output power as the corresponding value for each wind speed identifier With these pairs of values, i.e., points in a graph, the model can be defined as the function that best represents the points, by using an optimization process for an objective function which is the error made [29,30,31].
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