Performance of the Estimators Weighted Least Square, Extended Kalman Filter, and the Particle Filter in the Dynamic Estimation of State Variables of Electrical Power Systems

Abstract

The state estimation (SE) is a crucial subject in the analysis and management of electrical power systems (EPS). SE allows to determine and alert about unacceptable voltage and power flow values, network losses and network topology changes. Also, SE permits to know the available power and power flow in and amongst buses. This article provides a referential study for the performance of the Weighted Least Square (WLS), Extended Kalman Filter (EKF) and Particle Filter (PF) for comparisons between these techniques in the Dynamic State Estimation (DSE) of EPS. In the Extended Kalman Filter (EKF) technique, the Holt method is used to linearize the process model. The performance of the methods is analyzed through the comparison of the estimation results considering the error indexes ($\epsilon $). In this work, an IEEE 14-bus test case is utilized. The results show that the PF method has better accuracy than the WLS and EKF methods.