DPMU-based multiple event detection in a microgrid considering measurement anomalies

Abstract

A microgrid may be subjected to various unexpected events, such as sudden tripping of a generator/load, line outages due to faults, sudden switching of large capacitor banks, etc. Detection and classification of events play an essential role in the reliable operation, control, and restoration of microgrids. Due to low observability in microgrids, anomalies in measurements pose a challenge for reliable detection of events using conventional methods. This work proposes a data-driven approach for event detection in microgrids using phasor measurements collected from distribution-level phasor measurement units (DPMUs). The proposed method can determine whether the change in phasor measurement is caused by an event or due to any measurement anomalies. The model explores the interdependency among all phasor measurements collected from an unobservable microgrid. This method can identify and replace anomalies in DPMU measurements using a Rauch–Tung–Striebel (RTS) smoother. The proposed algorithm performance is validated on a test system developed in an OPAL-RT/HYPERSIM real-time simulator and phasor measurements from DPMUs.