2016

The frequency-domain analysis of the performance of a wind turbine that is placed in a mountainous complex terrain is presented. The variables of interest were the wind speed and the active power of the wind turbine, and the experimental data for the analysis were collected for three months with a sampling time of 10 minutes.

In this letter, nonparametric statistical inference is applied to designing a simple, inexpensive method of verifying the power performance of a wind turbine. The proposed method uses the data collected by the Supervisory Control and Data Acquisition (SCADA) system and the guaranteed power curve of the turbine, and it says whether the power performance of a wind turbine differed significantly from what would be expected.

In this paper, a method of verification of the power performance of a wind farm is presented. This method is based on the Friedman’s test, which is a nonparametric statistical inference technique, and it uses the information that is collected by the SCADA system from the sensors embedded in the wind turbines in order to carry out the power performance verification of a wind farm.

El presente trabajo tiene como objetivo materializar y evaluar el rendimiento de un nuevo prototipo de cocina solar.

El presente trabajo se realiza dentro del marco del proyecto “Evaluación de la contaminación atmosférica por fuentes de combustión en la ciudad de Loja y su mitigación con energías renovables” y tiene como objeto mostrar el criterio de selección de los puntos de medición para el monitoreo de gases procedentes de fuentes puntuales continuas, teniendo como antecedentes los resultados de las encuestas del trabajo de Rojas [1], y la modelación realizada utilizando el método de Gradiente K del Dr. M. E. Berlyand [2] modificado por Álvarez O. [3].

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.