Line outage identification-based state estimation in a power system with multiple line outages

Line outages in power systems cause data centers to miss some measurements thus making parts of networks unobservable. They also change the network topology and, consequently, classical state estimators do not converge. This paper proposes a new state estimation methodology based on the concepts of sparse vector recovery and line outage modeling. The proposed method first uses the pre- and post-outage voltage angle measurements to find the changes in network topology. Then it uses either the post-contingency data from PMUs and updated line flows or post-contingency measurements from PMUs and conventional PQ meters, as well as the updated topology, to provide an estimate of the power system state. The performance and accuracy of the proposed method are evaluated for the IEEE 24-, 57- and 118-bus systems. The simulation results show that the method provides good results for the three networks.