Power System State Estimation Under Model Uncertainty

Abstract

This paper considers the general state estimation in power systems when the system model is not fully known. Model uncertainty might be caused by lack of full information about the network model, or by unpredicted disruptions or changes to the grid topology, model, or parameters. This paper focuses on a setting for state estimation in which besides the nominal model, the system might follow a group of alternative models. Including alternative possibilities for the system model, introduces a new dimension to state estimation. Specifically, the state estimator needs to detect whether the system model has deviated from its nominal model, and if it is deemed to have deviated, then also isolate the actual model. These estimation, detection, and isolation decisions are inherently coupled due to the fact that isolating the true model is never perfect (due to noisy measurements), the effect of which transcends the isolation process, and affects the estimation routine as well. This paper establishes the fundamental interplay between the detection, isolation, and estimation routines, designs the optimal attendant rules, and provides an algorithm for implementing these rules in a unified framework. The optimal framework is applied to the IEEE 14-bus system model and IEEE 118-bus model, and the performance is compared against the existing relevant approaches.