Fast Real-Time DC State Estimation in Electric Power Systems Using Belief Propagation

TL;DR

This paper introduces a belief propagation-based real-time state estimator for electric power systems that is fast, distributed, and robust to measurement variance issues, enabling seamless processing of measurements.

Contribution

It presents a novel belief propagation algorithm for power system state estimation that is scalable, parallelizable, and eliminates the need for observability analysis.

Findings

Performs efficiently in real-time system models

Robust to ill-conditioned measurement scenarios

Can be extended to AC state estimation

Abstract

We propose a fast real-time state estimator based on the belief propagation algorithm for the power system state estimation. The proposed estimator is easy to distribute and parallelize, thus alleviating computational limitations and allowing for processing measurements in real time. The presented algorithm may run as a continuous process, with each new measurement being seamlessly processed by the distributed state estimator. In contrast to the matrix-based state estimation methods, the belief propagation approach is robust to ill-conditioned scenarios caused by significant differences between measurement variances, thus resulting in a solution that eliminates observability analysis. Using the DC model, we numerically demonstrate the performance of the state estimator in a realistic real-time system model with asynchronous measurements. We note that the extension to the AC state estimation is possible within the same framework.