Vectorized Gaussian Belief Propagation for Near Real-Time Fully-Distributed PMU-Based State Estimation
For power system operators, it enables scalable, distributed, and near real-time state estimation to improve monitoring and control, potentially preventing blackouts.
The paper proposes a vectorized Gaussian belief propagation framework for PMU-based state estimation in power systems, achieving near real-time, fully distributed operation with fast convergence and high accuracy, often within a single iteration, as demonstrated on systems with up to 13,659 buses.
Electric power systems require accurate, scalable, distributed, and near real-time state estimation (SE) to support reliable monitoring and control under increasingly complex operating conditions. Limited monitoring capabilities can lead to inefficient operation and, in extreme cases, large-scale disturbances such as blackouts. To address these challenges, this paper proposes a vectorized Gaussian belief propagation (GBP) framework for phasor measurement unit-based SE, formulated over factor graphs and specifically designed to support distributed and near real-time monitoring. The proposed framework includes multivariate and fusion-based GBP formulations. The multivariate formulation jointly models related state variables and their measurement relationships, while the fusion-based formulation reduces factor graph complexity by combining multiple measurements associated with the same set of variables, resulting in a structure that more closely reflects the underlying electrical coupling of the power system. The resulting algorithms operate in a fully distributed manner at the bus level and achieve fast convergence and high estimation accuracy, often within a single iteration, as demonstrated by numerical results on systems with 1354 and 13659 buses.