A Scheme Based On Pmu Data For Power Quality Disturbances Monitoring

Power quality (PQ) monitoring has attracted the interest of many researchers around the world. PQ disturbances (PQDs) such as sags, swells, interruptions, harmonics, notching, spikes, and oscillatory transients, among others, have to be detected and classified in order to apply a proper solution. For their detection and classification, many methodologies in literature have been proposed, where a signal processing technique and a pattern recognition algorithm are typically used. In this work, a new methodology for detection and classification of PQDs using a phasor measurement unit (PMU)-based scheme is presented. In general, the processing of voltage or current signals is carried out using a phasor estimation model (algorithm within a PMU), whereas the classification task is performed by threshold-based rules and an artificial neural network. The proposal is validated using synthetic signals. Then, it is tested using real measured signals. Results demonstrate its effectiveness and usefulness.