Spectrum-Based Optimal Filtering for Short-Term Phasor Data Prediction

This article describes a phasor data prediction and recovery algorithm that can be used to optimally extrapolate invalid or missing phasor data measurements. The basic output of the algorithm is the prediction of the phasor data when bad data quality is detected from a phasor measurement unit (PMU) at a given sampling rate. The approach is based on optimal one-sample prediction, where the sampling time between sample predictors is tuned based on the spectral content of phasor data. Optimality of prediction using such optimal filters is addressed in this article by iterative (recursive) search of the parameters of a nonlinear discrete-time integrated filter that minimizes the norm of the prediction error. The extrapolated data are then estimated by interpolation or reconstruction of the signal from the resulting optimally spaced predictors. Finally, a Kalman-filtering-based recovery method is discussed for smoothing the data for transitioning to actual valid measurements for use in real-time control applications. The approach is illustrated on phasor data obtained from a micro-PMU system developed by the Power Standards Lab collected at 60 Hz.