An extended Kalman filter for detecting voltage sag events in power systems

Abstract

Voltage sag event is one of the most important power quality disturbances in power systems. It can have affect on voltage quality and sensitive equipment in power systems. Detecting voltage sag events in power systems is a vital role in operating systems; therefore, this paper proposes an algorithm based on extended Kalman filter (EKF) for characterizing and detecting the parameters of voltage sag events accurately. A status-space modeling of voltage sag signals is defined to model voltage sag signal according to status-space modeling of EKF. The parameters of voltage sag events are estimated using the proposed method including voltage magnitude, estimation error, starting and ending times, duration time of the event. Matlab software is used to generate database of voltage sag waveforms modeled by a mathematical equation and then the waveforms are used to evaluate the proposed method. The simulation results of the proposed method are also compared with the simulation results of the root mean square (RMS) method to confirm the effectiveness of the proposed method in this paper.