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Methods to apply a 3-Parameter logistic model to wind turbine data

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Methods to apply a 3-Parameter logistic model to wind turbine data

Villanueva Torres, Daniel; Sixto, Adrián; Feijoo Lorenzo, Andres Elias; Fernandez Otero, Antonio; Miguez Garcia, Edelmiro
 
DATE : 2020-05-10
UNIVERSAL IDENTIFIER : http://hdl.handle.net/11093/1849
UNESCO SUBJECT : 2501 Ciencias de la Atmósfera ; 3322.05 Fuentes no Convencionales de Energía ; 1209 Estadística
DOCUMENT TYPE : article

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 ... [+]
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. [-]

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