The mutual coupling between the average wind speed, the turbulence intensity and the active power regulation affects the fatigue load of the wind turbine (WT), and the time series of the wind speed is random, which causes the uncertainty of the fatigu...
The mutual coupling between the average wind speed, the turbulence intensity and the active power regulation affects the fatigue load of the wind turbine (WT), and the time series of the wind speed is random, which causes the uncertainty of the fatigue load. In order to clarify the influence of multiple factors on the fatigue load of WT, this paper discusses the fatigue load of WT and its influencing factors based on data mining method. The Bladed software is used to combine various factors, and a large number of simulation experiments are carried out to obtain the fatigue load data distributed in the three directions of the four components of the blade, hub, yaw and tower. Kernel density function is employed to analyzed distribution of fatigue load data. The fatigue load data model is established to find out the influence of average wind speed, turbulence intensity and active power regulation on fatigue load.