Towards Robust and Scalable Power System State Estimation

Power system state estimation is an important instance of data-driven decision making in power systems. Yet due to the nonconvexity of the problem, existing approaches based on local search methods are susceptible to spurious local minima. In this study, we propose a linear basis of representation that succinctly captures the topology of the network and enables an efficient two-stage estimation method when the amount of measured data is not too low. Furthermore, we develop a robustness metric called "mutual incoherence," which provides robustness guarantees in the presence of bad data. The proposed method demonstrates superior performance over existing methods in terms of both estimation accuracy and bad data detection for an array of benchmark systems. This technique is shown to be scalable to large systems with more than 13,000 nodes and can achieve an accurate estimation within a minute.