Abnormal event detection with high resolution micro-PMU data
With the unprecedented growth of renewable resources, electric vehicles, and controllable loads, power system has been incorporating increasing amount of unconventional generations and loads. As a consequence, signiﬁcant dynamic and stochastic power ﬂow are introduced into distribution network, requiring high resolution monitoring technology and agile decision support techniques for system diagnosis and control. In this paper, we discuss the application of micro-synchrophasor measurement unit (µPMU) for power distribution network monitoring, and we propose a novel data-driven method, namely Ensembles of Bundle Classiﬁer (EBC), for event detection. The main idea is: multiple classiﬁers are learned each with a short slot of µPMU measurement generated by a single event. Then their decisions are combined with a “winner-takes-all” scheme. This framework naturally resolves the challenging issue of heterogeneity in the high resolution µPMU data, and signiﬁcantly outperforms classic data-driven event detection methods. In this paper, the proposed framework is tested on an actual distribution network with µPMUs, and is compared to other state-of-the-art methods. The result justiﬁes the effectiveness of EBC as a promising tool to improve the security and reliability of distribution network.