Posters - WindEurope Technology Workshop 2022
Resource Assessment & Analysis of Operating Wind Farms 2022
23-24 June • Brussels


Come meet the poster presenters to ask them questions and discuss their work

Check the programme for our poster viewing moments. For more details on each poster, click on the poster titles to read the abstract.

PO073: Power curve of wind farm fitting base on Gaussian mixture distribution and S-curve

Gang Huang, Research engineer, Meteodyn


For a wind turbine, there is a corresponding curve to represent the relationship between the turbine output power and the wind speed (this relationship is similar to an S-shaped curve when the output power does not reach the rated power), which is called turbine power curve. For a wind farm, the relationship between the wind farm output power and the wind speed is also similar to an S-shaped curve. In this study, the wind farm output power is calculated by the sum of the output power of all turbines in the wind farm and the wind speed of the wind farm is defined as the wind speed measured by the mast at the wind farm. This curve, which called the wind farm power curve, can be obtained by fitting the historical whole farm power and wind speed data. Wind farm power curves have many applications. For example, the evolution of wind farm power generation performance can be analyzed by comparing the wind farm power curves over the years, so that the future power generation plan can be adjusted in time. Based on long-term wind speed forecast data, it is possible to predict future wind farm power forecast time series data by using the wind farm power curve, and get the general trend of power generation in the future. During the fitting of wind farm power curve, points that deviate from the trend of wind farm power curve may appear on the figure of power vs. wind speed due to different reasons such as turbines shutdown, power curtailment or data acquisition error in the wind farm, which brings many errors when fitting power curve. Of course, outliers can be filtered out manually, but if the data length is very long, it must be a very time-costly work. In the power vs. wind speed figure, it is assumed that the points in each power bin follow a mixture of Gaussian distributions, including outliers. The Gaussian mixture model can help to find the mean value (2-dimensions) of every distribution in each power bin. We use these mean values to represent the trend of wind farm power curve, since it reduces the influence of outliers on fitting wind farm power curve. Finally fitting wind farm power curve using these mean values with 3-parameters S-curve (these 3 parameters are the main parameters for adjusting the S-shaped curve). The advantage of this method is that it can give a credible wind farm power curve without manually removing outlier data. Based on a dataset of wind power output of 10 wind farms, the performance of this method in analyzing the power generation yearly performance of wind farms and forecasting wind farm power can be tested.