A robust extended Kalman filter for the estimation of time varying power system harmonics in noise

Measurement of time varying harmonics has assumed great importance for power quality estimation in micro and smart grid environments. It is well known that the power signals undergo dynamic transitions during power quality and fault type disturbances. Also accurate estimation of amplitude, phase and frequency of a sinusoid in the presence of harmonics/inter harmonics and noise plays an important role in wide variety of power system applications, like protection, power quality control and state monitoring. One of the widely used approach like the Fourier linear combiners become ineffective to track the sudden changes in the power harmonic amplitude and phase angles in the presence noise and distortions. This paper, therefore, presents a new adaptive robust Kalman filter (REKF) for estimating the fundamental and harmonic phasors and frequency in a distributed generation systems suitable for the detection of islanding, nonislanding and switching events. The REKF is based on linear H-infinity technique to avoid the error divergence due to linearization and Jacobian formulation in the usual extended Kalman filter (EKF). Further the noise covariance matrices of the robust Kalman filter are tuned iteratively to produce faster convergence and speed of accuracy in tracking time varying harmonic phasors.