Scalable and Robust State Estimation from Abundant but Untrusted Data

We propose a linear representation that captures grid topology and enables an efficient two-stage estimator for power system state estimation from abundant but potentially untrusted data. We derive an identifiability condition delineating when the unique global optimum can be efficiently recovered, and introduce a robustness metric—mutual incoherence—to analyze global recovery and statistical error bounds under dense noise and bad data. The method outperforms prior approaches in accuracy and robustness, scales to >13,000-bus systems, and achieves minute-level runtimes.