Abnormal event detection with high resolution micro-PMU data

Abstract

Abstract-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-theart methods. The result justifies the effectiveness of EBC as a promising tool to improve the security and reliability of distribution network.

The FUSE testbed: establishing a microgrid for smart grid security experiments

“…The C37.118.1 standard addresses the measurement aspects of voltage phasors, frequency, rate of change of frequency, while the C37.118.2 standard addresses the communication aspects for data exchange. Analytics and diagnostic applications based on PMU data include phase angle deviations, oscillation monitoring, voltage stability monitoring, fast frequency decline, sudden change of active/reactive flows, cascading events, topology detection and others, whereas control applications may include microgrid controlling, intentional islanding, and Volt-Var optimization [1,6].…”

Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

“…Recent works have shown the advantages of using big data and machine learning applications in power quality analysis [5]. There are several intelligent methods presented such as shape-based data analytics of event signals [6,7], non-parametric and partial-knowledge detection [8][9][10], and also preliminary studies including intelligent classifiers [11][12][13][14].…”

Power Quality Event Detection Using a Fast Extreme Learning Machine

“…In literature, there has been many studies based on transform and model-based methods [14][15][16][17][18][19]. In addition to data-driven methods, such models using micro-synchrophasor measurement data [20] are also proposed. Conventionally, Fast Fourier Transform (FFT) and Root Mean Square (RMS) variation tracking methods exist and have a long-term usage in feature extracting [21,22].…”