Forecasting-Aided State Estimation in Power Systems During Normal Load Variations Using Iterated Square-Root Cubature Kalman Filter

Abstract

The main aim is to estimate the voltage profile at all buses in the system before the next set of hybrid measurements arrives from the field. The effectiveness of the proposed ISCKF algorithm during load variations is evaluated and compared with existing Kalman filter approaches for this application, including sudden load changes that occur in practical power systems. This study investigates the use of an Iterated Square-root Cubature Kalman Filter (ISCKF) for power system Forecasting-Aided State Estimation (FASE) during normal load variations. Its implementation involves embedding the Newton–Gauss iterative method into the Square-root Cubature Kalman Filter (SCKF) at the measurement update step. The square-root factor of error covariance matrices is calculated using QR decomposition to preserve the positive definiteness of the matrix. Estimation is carried out using hybrid measurements from remote terminal units and phasor measurement units. The state vector is forecasted between the arrivals of two measurement sets, thereby enabling state estimation of bus voltages even when measurements are unavailable. The efficacy of the proposed algorithm is evaluated on the IEEE 30-bus system and the Northern Region Power Grid (NRPG) 246-bus system. Simulation results show that the proposed ISCKF outperforms the CKF with a significant improvement in the accuracy of forecasting-aided state estimation. ISCKF provides results before the next set of hybrid data arrives, as expected from a state estimation algorithm. Therefore, the proposed algorithm is applicable for real-time implementation in large power systems.