Research On Wind Power Prediction Algorithm Of Wind Farm

In the process of large-scale integration of wind power,such as volatility and intermittence,will seriously affect the power flow section of large-scale power grid and the power quality of power system,and even more seriously,it will endanger the safe and stable operation of power system.In order to reduce the impact of large-scale wind power integration on large-scale power grid,the development and arrangement of reasonable wind power dispatching curve can effectively avoid the impact of wind power fluctuation on large-scale power grid.Wind power prediction can effectively reduce the operating costs of wind farms,improve the advantages of wind power participating in grid bidding,and improve the impact of large-scale wind power integration on the power system.Therefore,the research of wind power prediction based on artificial intelligence algorithm and the optimization of prediction model have important practical significance for improving the accuracy of wind power prediction and the reliability of grid connected dispatching,and has good academic value and engineering practical value.In this thesis,the short-term,medium-term and long-term prediction methods of wind power are taken as the research object,and the main research work is as follows:(1)This thesis introduces the preprocessing process of wind power historical data,and extracts the feature of the original data after preprocessing,so as to separate the high and low frequency signals and get the modal function.Finally,the evaluation index of wind power prediction model is introduced;(2)The short-term prediction of wind power is studied based on the neural network algorithm of empirical mode decomposition.Based on empirical mode decomposition,the original data of wind power after data preprocessing is extracted by feature quantity;after the feature quantity is extracted,the feature quantity is input into neural network model algorithm for training to complete the prediction of wind power;and taking the historical data of a domestic wind farm as an example,the prediction model is effectively verified with high prediction accuracy;(3)Based on empirical mode decomposition and grey prediction algorithm,wind power medium and long-term forecasting model is proposed.In order to verify the effectiveness of the medium and long-term wind power prediction model based on empirical mode decomposition and grey prediction algorithm,through the analysis of the RMSE and MAPE error calculationresults of four prediction models GM(2,1),EMD,EMD-BP and GM(1,1),it is verified that the second-order grey prediction model based on empirical mode decomposition can achieve accurate prediction of wind power series.(4)On the basis of Chapter 3 and Chapter 4,the software development of wind power prediction system is completed based on MATLAB simulation software,and the system software is tested through wind power sampling data.The wind power prediction system software with the functions of user identity and authority,data acquisition function,prediction model establishment,data processing function,prediction and error analysis is constructed.The research results of this thesis can provide technical support for wind power prediction.There are 31 figures,4 tables and 84 references in this thesis.