Research On Ultra-Short-Term Power Prediction Of Wind Power Basedon Variational Mode Decomposition Combined Model

As one of the renewable clean energy,wind energy has many advantages,such as easy mining,low transportation cost,no pollution and so on.With the global advocacy of low-carbon economy,China has made great efforts to develop wind power construction in recent years.The installed scale of wind power is increasing year by year and continues to enter the grid.However,due to the fluctuation and intermittence of wind speed,the output of wind power is unstable,which brings great difficulties to power dispatching;and when the proportion of wind power increases to a certain proportion,the security risk of power grid operation will be increased.Therefore,how to improve the grid's ability to accept wind power is a problem that has been discussed in the industry.In this context,wind power forecasting emerges as the times require.Integrating the methods of wind power forecasting at this stage,it is not difficult to find that there are mainly shortcomings such as low forecast accuracy and short forecast duration.This article studies how to improve the accuracy of wind power ultra-short-term power forecasting.The main contents are summarized as follows:First of all,the background of the research topic,the significance of the research and the current research status are briefly introduced.Combined with the basic characteristics of wind energy and the relevant technical parameters of wind turbine,the main factors affecting the power prediction of ultra-short-term wind power generation are analyzed and summarized.Then it introduces the commonly used wind power prediction methods,and puts forward the indirect prediction method,that is,the wind speed is predicted in multi steps.Secondly,aiming at the problems of low accuracy and poor response ability of wind speed prediction model caused by the characteristics of wind speed fluctuation and instability,this paper uses the variational mode decomposition(VMD)algorithm to decompose the original wind speed data on the time series,and obtains the sub series with good stability.The simulation results show the effectiveness of the method.Thirdly,on the basis of the variational mode decomposition algorithm,BP(back propagation)neural network algorithm is introduced to establish the prediction model for the decomposed subsequences,and the direct multi-step prediction method is adopted for multi-step prediction of each component,and then the prediction results of each wind subsequence are superimposed and reconstructed to obtain the wind speed prediction results.The simulation results show that the ultra-short-term wind speed prediction method based on VMD-BP model has high accuracy.At last,the support vector regression(SVR)is used to fit the wind speed power mapping curve.Combined with the wind speed prediction method of VMD-BP model,the ultra-short-term wind power prediction model is established.The simulation results show that the ultra-short-term wind power prediction method based on the variational mode decomposition combination model(VMD-BP)has high prediction accuracy.