Study Of Short-term Wind Power Prediction

In recent years,with the rapid increase of wind power installed capacity in China,large-scale wind power grid is connected to the grid,and the random fluctuation of wind power brings great challenges to the stable operation of power grid.In order to reduce the adverse effects of wind power integration on the grid,and ensure that the grid can be reasonably scheduled and stable operation,it is very important to carry out the research of wind power prediction.At home and abroad,the research of wind power prediction has been a long time,mainly from two aspects:physical modeling and statistical modeling.Most of the current domestic wind farms are in terrain complex areas,and lack of the wind field information needed for physical modeling,so the wind power prediction is mostly based on the wind field historical data.Because of the outstanding performance of artificial neural network and support vector machine,the two kinds of statistical methods are used to simulate the wind field in the paper.However,there are large errors and imprecise problems in the prediction of a single statistical method,the two methods are improved on the basis of the original method.At the same time,the paper studies the uncertainty of wind power prediction result based on the above two kinds of prediction results.Based on the sample data of historical wind power,the paper studies the wind power prediction as follows:1.Using neural network prediction model to predict the wind power output,using the Markov model to modify the prediction error,and then constructing the RBF-Markov model to predict the wind power output.2.Using particle swarm optimization algorithm(PSO)to complete the parameter optimization of bat algorithm,analyzing the influence of parameters on support vector machine(SVM),then using the improved PSO algorithm to optimize support vector machine,constructing the BAPSO-SVM prediction model to make up for the shortcomings of the single prediction model and predict the wind power output.3.Combining RBF-Markov model and BAPSO-SVM model to study the probabilistic forecasting model of wind power.In order to avoid the problem that the forecasting accuracy of wind power is not high enough,in the paper,based on the above two kinds of prediction results,the interval prediction of wind power is carried out.The nonparametric kernel density estimation method is used to estimate the probability density function of the error,and the confidence interval is obtained.The range of daily power fluctuation is obtained,so as to improve the accuracy of the prediction.