A method for extracting steady state components from Syncrophasor data using Kalman Filters

Data from Phasor Measurement Units (PMUs) may be exploited to provide steady state information to the applications which require it. Raw PMU data is polluted with noise and cannot be directly fed to applications without adequate processing. This paper presents a method to extract steady state components from Syncrophasor data using Kalman Filters (KF). This method is capable of reducing the noise, to compensate for missing data and filtering of outliers in signals. The Residue in each KF iteration is computed. The measurement noise covariance matrix R is calculated by computing the variance of residue using rolling windows. The performance of presented method is evaluated by using PMU data generated from a Hardware-In-the-Loop (HIL) experimental setup.