A New Prediction Method of Wind Power Based on L2 Norm Cloud to Erase the Wind Uncertainty - Fangyu Wang; Liping Zhu; Wenying Liu; Jie Qin (IEEE Access)
Due to the large dispersion and uncertainty of wind power operation, it is very difficult to predict the wind power. Even though there are many prediction methods considering the uncertainty of wind power, there is still not a method to accurately fit the corresponding relationship between wind speed and wind power, and thus the wind power prediction is not accurate. According to this paper, the probability density curve of wind power under different wind speeds is firstly studied, and the combined cloud model is established with the peak value as the boundary to represent the uncertain corresponding relationship between wind speed and power. Secondly, in order to avoid the error increase of the combined cloud model caused by the sample peak disturbance, L2 norm theory is introduced to update the peak point to enhance the robustness of the model. Finally, The parameters of L2 norm cloud model are calculated based on Bayesian theory. The simulation results show that the combined cloud fitting method can obtain high fitting accuracy for the irregular single peak or multi peak wind speed power probability distribution with uncertainty.