ESTIMATING THE DYNAMIC STATES OF MULTI-MACHINE POWER SYSTEM USING EXTENDED AND UNSCENTED KALMAN FILTER

Meera Karamta and Jitendra Jamnani

Keywords

Power system state estimation, Dynamic state estimation, Multi-machine, Extended Kalman filter

Abstract

A reliable, stable, secure and efficient operation is reliant on the process of state estimation.. There is an exemplar shift in the concept of power system state estimation; from static to dynamic. Dynamic State Estimation (DSE) is a process of estimating the dynamic state variables; vis-à-vis generator states to provide enhanced control. Additionally, improvised data acquisition (with smaller data sampling time) through Phasor Measurement Units (PMUs) over conventional Supervisory Control And Data Acquisition (SCADA) system enables application of newer state estimation techniques. The faster availability of power system measurements assists in application of newer generation of Dynamic state estimation algorithms. This paper is an attempt to implement the widely used extensions of Kalman filter; Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) for dynamic state estimation. These techniques are applied to standard WSCC-9 bus, IEEE-14 bus & IEEE-30 bus systems to estimate the generator dynamic states. A classical generator model is used and the estimated state variables of generator are generator rotor speed and rotor angle. A brief comparison is made for the two methods based on overall and mean estimation errors.

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