PERFORMANCE ASSESSMENT OF EKF AND UKF BASED DYNAMIC STATE ESTIMATORS FOR INTERMITTENT MEASUREMENT DATA IN POWER SYSTEM

Accurate dynamic state estimation(DSE) is prime need for better monitoring and control of highly complex power system. Extended Kalman filter(EKF) and unscented Kalman filter(UKF) based algorithms are efficient tool of DSE using phasor measurement unit(PMU) based measurement data in power system. This research focuses on comparative analysis of estimation ability of EKF and UKF under the conditions of intermittent measurement data. Results recommend trade-off between duration of inconsistent measurement data and measurement data update rate to ensure converged estimation in data unavailability conditions. Simulation results are endorsed using two standard multi-machine test systems viz. WSCC 3-Generator 9- bus system and IEEE 14 bus system.