In order to further improve the dynamic identification accuracy of power grid abnormal data,a dynamic identification method of power grid abnormal data based on the clustering method of neighborhood local abnormal factors-Gaussian mixture model was designed by combining the Gaussian mixture model and using the neighborhood local abnormal factors to determine whether the point belongs to abnormal data.The research results show that the designed neighborhood local anomaly factor-Gaussian mixture model clustering algorithm effectively meets the needs of the dynamic cleaning process of distribution network big data integration,obtains more accurate load forecasting results,and helps to significantly enhance the responsiveness of the distribution network.The designed method achieves a balance between accuracy and speed in filling missing data,and has good engineering application value.
关键词
配电网/数据清洗/异常数据辨识/高斯混合模型/随机森林
Key words
distribution network/data cleaning/abnormal data identification/gaussian mixture model/random forest
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