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Sensors 2016, 16(12), 2080;

Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine

Departamento de Ciencias de la Computación y Electrónica, Universidad Técnica Particular de Loja, Campus de la Universidad Técnica Particular de Loja, Calle San Cayetano Alto s/n, Loja 1101608, Ecuador
Departamento de Matemática a las Tecnologías de la Información y Comunicaciones, Universidad Politécnica de Madrid, Av. Complutense Nº 30, 28040 Madrid, Spain
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 26 September 2016 / Revised: 16 November 2016 / Accepted: 23 November 2016 / Published: 7 December 2016
(This article belongs to the Section Physical Sensors)
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Having an accurate model of the power curve of a wind turbine allows us to better monitor its operation and planning of storage capacity. Since wind speed and direction is of a highly stochastic nature, the forecasting of the power generated by the wind turbine is of the same nature as well. In this paper, a method for obtaining a robust confidence band containing the power curve of a wind turbine under test conditions is presented. Here, the confidence band is bound by two curves which are estimated using parametric statistical inference techniques. However, the observations that are used for carrying out the statistical analysis are obtained by using the binning method, and in each bin, the outliers are eliminated by using a censorship process based on robust statistical techniques. Then, the observations that are not outliers are divided into observation sets. Finally, both the power curve of the wind turbine and the two curves that define the robust confidence band are estimated using each of the previously mentioned observation sets. View Full-Text
Keywords: SCADA system; power curve; power-curve confidence band SCADA system; power curve; power-curve confidence band

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Hernandez, W.; Méndez, A.; Maldonado-Correa, J.L.; Balleteros, F. Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine. Sensors 2016, 16, 2080.

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