Research On Short-term Power Forecasting And Slope Climbing Recognition Method Of Wind Farm

Wind power has the characteristics of fluctuation,and its large-scale grid-connected easily leads to the stability of the whole power grid greatly reduced.Wind power forecasting and slope climbing analysis can predict and analyze the future wind power and slope climbing situation in advance,and provide reference for the planning and control of dispatching in advance,so as to effectively prevent grid stability problems caused by wind power fluctuation.Therefore,it is of great significance for the further development of wind power to study how to improve the accuracy of wind power prediction and how to identify and analyze climbing events.Taking a wind farm as an example,firstly,data analysis and cleaning are carried out.Five statistical learning methods,including fitting regression method,depth confidence network(DBN),BP neural network,support vector machine and Elman neural network,are used to predict and model its wind power.The forecasting effects and advantages and disadvantages of different algorithms are compared and analyzed.In view of the shortcomings of the five prediction models,based on the analysis of similar time,a new deviation correction algorithm is proposed by using clustering method,median method and regression fitting method to analyze the characteristics of one prediction error.This method combines the above four primary prediction models of wind power,and constructs the secondary prediction process of wind power based on the combination model of deviation correction algorithm.The results show that the deviation correction algorithm can correct the primary prediction error and improve the secondary prediction accuracy,but the prediction accuracy of the existing models is poor in the high wind speed section.Aiming at the problem that the existing wind power prediction models generally have poor power prediction accuracy in the high wind speed section,a method for identifying the wind power climbing section is proposed.Firstly,the existing identification methods of climbing events are analyzed and compared.Aiming at the problem that the old identification methods are difficult to distinguish the power climbing interval from the gentle interval,the concept of "stagnation point" is introduced,and a new definition of "climbing segment" is proposed.To solve the problem of empiricizing the setting of climbing threshold,the setting of climbing threshold is discussed by means of mathematical statistics;an extremum extraction algorithm is proposed,and a new definition of "climbing segment" is proposed.A wind power climbing segment recognition model based on extremum point extraction is constructed.The validity and practicability of the identification method of wind power climbing section based on extremum point extraction are verified by analyzing the characteristics of the climbing range,climbing speed and duration of the actual wind farm climbing section.