Research on Bad Data Detection Algorithm of PMU Device under Large Disturbance Condition of DBSCAN+LOF
In view of the problem that the traditional k-means algorithm for outlier detection is prone to false detection and false judgment in the case of large disturbances,a DBSCAN+LOF based algorithm for detecting bad data of PMU in power system is proposed.The results show that the normal PMU data have strong spatial and temporal similarity,the poor PMU data have weak spatial and temporal similarity,the large disturbance PMU data have strong spatial similarity,but weak temporal similar-ity.According to the space-time characteristics of the three types of data,the DBSCAN algorithm is used to detect outliers,the LOF algorithm is used to calculate local outlier factors,and the large disturbance PMU data and PMU bad data can be discrimi-nated by the size of local outlier factors.The algorithm proposed is applied to the short-circuit fault of the power system.The results show that at the time of occurrence and removal of the short-circuit fault,the LOF calculation results are shown as large disturbance PMU data.After the fault is removed,the LOF calculation results are shown as PMU bad data.The detection re-sults are completely consistent with the actual situation.The algorithm is reasonable and effective.
power systemPMU bad datalarge disturbancedetection algorithmDBSCANLOF
万方数据
维普
