Extended Kalman Filtering Based Real-time Dynamic State and Parameter Estimation Using PMU Data

This paper was accepted by Electric Power Systems Research on May 27, 2013
Authors: Lingling Fan and Yasser Wehbe
This paper proposes extended Kalman filtering (EKF) based real-time dynamic state and parameter estimation using Phasor Measurement Unit (PMU) data. In order to reduce computing load, model decoupling technique is used where measurements (real power, reactive power, voltage magnitude and phase angle) from a PMU are treated as inputs and outputs from the system. Inputs are real and reactive powers while outputs are voltage magnitude phase angle. EKF is implemented using a second-order swing equation and a classical generator model to estimate the two dynamic states (rotor angle and rotor speed) and unknown parameters, e.g., mechanical power, inertia constant, damping factor and transient reactance. An EKF algorithm is developed using model decoupling technique for real-time estimation of states and parameters related to electromechanical dynamics. The EKF based estimation can estimate two dynamic states along with four unknown parameters.