The wind turbine power curve WTPC describes the relationship between wind speed and turbine power output. Power curve, provided by the manufacturer is one of the most important tools used to estimate turbine power output and capacity factor. Hence, an accurate WTPC model is essential for predicting wind energy potential. This paper presents a comparative study of various models for mathematical modelling of WTPC based on manufacturer power curve data gathered from 32 wind turbines ranging from 330 to 7580 kW. The selected models are validated by comparing the capacity factor obtained using the models based on Gamma probability density function with the capacity factor estimated using manufacturer power curves based on measured wind speed data. The selected models are also validated by comparing the instantaneous power obtained using the models with manufacturer power curve data. The accuracy of the models is evaluated using statistical criteria such as Normalized Root Mean Square Error (NRMSE), relative error (RE), and correlation coefficient (). The adopted model allows predicting the behavior of wind turbine generated under different wind speeds. Results of the analysis presented in this paper show that the power-coefficient based model presents favorable efficiency followed by general model, since they have lower values of RE in estimation of capacity factor, whereas the polynomial model showed the least accurate model.
The main objective of this paper is to analyze the statistical wind speed data recorded in Zuwara-Libya during 2007, the wind speed measured at three hub heights of 10m, 30m, and 50m above the ground, wind speeds was taken every 1 minute, were averaged over 10 minutes. The wind speed data set is analyzed using Weibull, Rayleigh, and Gamma distribution. An effort has been made to find out the best fitting distribution of wind speed data, which are evaluated by using two goodness of fit tests, namely, Chi-Squared test, and Kolmgorov-Smirnov test. Root mean square error, and correlation coefficient are also used to determine error and describe the correlation between the observed data and each distribution. From analysis it is concluded that the Weibull distribution gives the best fitting for observed wind speed.
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