基于天气分型聚类和特征参数提取的分布式光伏识别方法OA

In recent years,with the continuous development of new energy generation technology,distributed photovoltaics(DPVs)have gradually become popular among residents.However,a large number of"unmonitored"DPVs pose great challenges to the safe and stable operation of the power grid.In order to improve the identification accuracy of DPVs,a method based on weather type clustering and the extraction of net load curve feature parameters is proposed in this paper.Firstly,K-means clustering is used to cluster and analyze the net load data of users according to four different weather conditions,and the net load curves for the corresponding weather conditions are obtained.Then,characteristic parameters of the net load curves are extracted,and the random forest algorithm is employed to establish a DPV identification model.Finally,by integrating various characteristic parameters and evaluation indicators to determine whether the user has installed distributed photovoltaics,it can be concluded that the accuracy of the proposed method is higher than that of other identification methods.