PMUs and SCADA Measurements in Power System State Estimation through Bayesian Inference

Abstract

Phasor Measurement Units (PMUs) in transmission systems is one of the most promising sources of data to increase situational awareness of network monitoring. However, the inclusion of PMU measurements along with the ones from traditional Supervisory Control and Data Acquisition (SCADA) systems to perform state estimation brings additional challenges, such as the vast difference in sampling rates and precision between these two types of measurements. This paper formally introduces a Bayesian inference approach in the form of a new State Estimator for transmission systems able to deal with the different sampling rates of those measurements. The proposed approach provides accurate state estimates even for buses that are not observable by PMU measurements, and when load variation occurs during the time interval between two SCADA data scans. Several simulation results (with IEEE transmission test systems) are used to illustrate the features of the proposed approach.