Power Curve Characterization II. Modelling Using Polynomial Regression

Abstract

Abstract. Accurate determination of wind turbine performance is necessary for economic operation of a wind farm. We propose modifications to the basic polynomial method to take account of distance between the meteorological mast and the wind turbine and also the complexity of the terrain. The methods are evaluated using data from operational wind farms and the methods are compared to a modified IEC 61400-12 bin method.

Equivalent Wind Farm Power Curve Estimation

“…To get the characterization some k values have been tested from 0.5 to 3, in steps of 0.5. Each bin has been characterized for the k value that optimizes the mean squared error, obviously the data taken to calculate the error are only those that belongs to this bin [3][4][5][6]. Figure 4 shows the distribution of wind direction at this wind plant as measured by wind vanes on the MET tower.…”

Performance prediction of a multi-MW wind turbine adopting an advanced hydrostatic transmission

“…This great deal of data was filtered and processed according to the bin method, as outlined in the IEC (International Electrotechnical Commission) 61400-12-1 international standard [21] and applied in Refs. [22,23], in order (i) to eliminate acquisition errors and (ii) to obtain the power curve of the real turbine along with the rotational speed and pitch angle characterization. The dispersion of the experimental power data for a given wind speed was evaluated by means of the standard deviation that resulted in an average value of 58.7 kW.…”

Statistical power curve modeling to estimate wind turbine power output

“…Polynomial regression (Llombart et al, 2006) is a simple way to provide a nonlinear fit between the independent variable, that is, wind speed, and the dependent variable, that is, wind power. It extends the linear model by adding extra independent variables, derived by raising each of the original independent variables to a power.…”