Dynamic state estimation of power system harmonics

The application of solid state drives in power systems has led to numerous issues of electric power quality. Many of these issues are related to harmonics in power systems which threaten the quality of electric power supplied to the consumer. The upgrading of the IEEE guide that set limits on harmonic injections added additional focus to the subject of harmonic state estimation and monitoring. This research gives a comprehensive and deep study of power system harmonic state estimation. In particular, dynamic state estimation of power system harmonics is the subject of this research. Dynamic state estimation algorithms have been developed to estimate and track power system harmonic changes as a result of load variation over a period of 24 hours. Kalman filter methodologies have been used to get the optimal estimate of the state vector; harmonic bus voltage magnitudes and phase angles. Two types of Kalman filter techniques have been used. First, is the centralized Kalman filter technique, in which the measurement vector is processed by one estimator (processor). This technique is usually applied to small-size power systems. Second, is the decentralized structure of Kalman filter, in which the measurement vector is partitioned in several subvectors, each is processed by a local estimator. This approach is potentially fast and reliable when implemented using parallel multi-processor