Method for analysing of wind power wave nature based on kernel density estimation

Abstract

Abstract It is beneficial for improving the accuracy of wind power output prediction by analysing and mastering the inherent laws of the fluctuation characteristics of wind power output, guiding the power grid dispatching department to reasonably arrange power generation plans, and improving the economic efficiency of system operation. In order to characterize the probability density distribution of wind power output fluctuation, two adaptive bandwidth kernel density estimation models are established by correcting the fixed bandwidth obtained from the empirical method and unbiased cross-validation method respectively, and then the two models are combined and optimized, and ultimately, the probability density distribution model of wind power output fluctuation based on Hybrid Adaptive Kernel Density Estimation (HAKDE) is established. A variety of probability density distribution models are used to fit the wind power output fluctuations at different spatial and temporal scales in a province in North China, and the example results show that the hybrid adaptive kernel density estimation model has the best fitting effect, thus verifying the effectiveness of the hybrid adaptive kernel density estimation model.