Extended Kalman filtering based real-time dynamic state and parameter estimation using PMU data

•We developed EKF model for a synchronous generator, which is decoupled from the rest of the network.•We implemented PMU data to estimate states and parameters using the EKF model.•Electromechanical dynamics related states and parameters can be estimated with accuracy.•Two states (rotor angle and speed) and four parameters (mechanical power, damping factor, inertia and transient reactance) can be estimated in 5–10 s.

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.