Optimal data screening and bad data identification based on sensitive analysis

To detect and identify bad data, the measured data of multi-time scale is screened by integrating graph theory with ant colony optimization (ACO). Firstly, the measurement is changed into paths and the data of the whole network is equivalent to a undirected graph; then combining ACO with the search method of minimum spanning tree (MST) in graph theory, all measured data is screened and during the iteration the power network status and the standardized residuals of measurement are achieved by sensitivity analysis, and the comparison of standardized residuals of the data and that of system status are implemented during the iteration system status; in the form of searching, the optimal measuring combination can be found and the system status at that moment caqn be calculated; finally, utilizing sensitivity analysis the bad data is detected and identified. The proposed algorithm can avoid the time loss brought by repetitive state estimation during the identification while the computational accuracy is kept. The proposed algorithm is verified by the simulation of IEEE 14-bus system.