Forecasting-Aided Power System State Estimation Using Multisource Multirate Measurements

This article presents a multistage forecasting-aided state estimation (FASE) framework with equality constraints, utilizing the extended Kalman filter (EKF) to independently process supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) data. The conventional SCADA-based state estimator remains unaltered and is augmented with synchronized PMU data and a priori state information derived from the EKF. An estimation fusion technique leveraging real-time PMU measurements is developed to enhance the accuracy of the FASE forecasting model. To address measurement asynchronization and random delays, the modified Bryson-Frazier (MBF) fixed-interval smoothing algorithm is applied, combining current state information with a series of prior FASE solutions to optimally infer the system states. Extensive numerical simulations on the IEEE 14-, 118-, and 300-bus benchmark systems demonstrate the efficacy and practical applicability of the proposed method.