Short-term Wind Farm Power Prediction Method Based On K-means Clustering And Hierarchical Analysis

In recent years,due to the increasingly serious energy shortage and environmental pollution,people are paying more attention to clean energies such as wind and the scale of wind power integration grows a lot.But because of the characteristics of wind power like randomness and uncertainty,wind power integration directly influences the safety and stability of power grid operation.Accurate prediction of wind power can provide a reference for the data of grid operation and is beneficial to the allocation and dispatch of power grid.Therefore,this thesis,targeted at wind power prediction,proposes a wind farm short-term power prediction method based on K-means clustering-hierarchy analysis.First,study the wind power generator model,wind resource characteristics and wind farm output probability distribution.Then,establish the wind farm short-term power prediction model based on K-means clustering-hierarchy analysis.Implement clustering for the wind farm in the research area by means of K-means clustering algorithm and establish rectangular coordinate system with geographic position information as classification indicator and the center of the research area as origin,thereby obtaining the geographic position coordinate of the wind farm and conducting clustering analysis.Analyze the wind speed relevance among different wind farms and take the influences of factors such as altitude,topography,temperature,humidity,etc.on wind speed into consideration.Adopt analytic hierarchy process to analyze the weights of all influence factors and establish correspondent analytic hierarchy model.Moreover,on the basis of the wind speed and weather and geographic information of a certain wind farm,use wind farm short-term speed prediction model based on K-means clustering-hierarchy analysis to forecast the wind speed of other related wind farms in this area and validate and compare it with the actual wind speed of the wind farm.According to the predicted wind speed,forecast the power of the wind farm in combination with the installed capacity of the wind turbine generators.At last,analyze the error of the prediction results through examples and verify the effectiveness of the algorithm.