Dynamic Identification and Analysis of Abnormal Data in Power Grid Based on LOF-GMM Method
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.
distribution networkdata cleaningabnormal data identificationgaussian mixture modelrandom forest
万方数据
维普
