Detection and Analysis of Multiple Events Based on High-Dimensional Factor Models in Power Grid
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Department of Electrical Engineering, Center for Big Data and Artificial Intelligence, State Energy Smart Grid Research and Development Center, Shanghai Jiaotong University, Shanghai 200240, China
2
Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA
*
Author to whom correspondence should be addressed.
Energies 2019, 12(7), 1360; https://doi.org/10.3390/en12071360
Received: 21 March 2019
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Revised: 31 March 2019
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Accepted: 5 April 2019
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Published: 9 April 2019
(This article belongs to the Special Issue Data-Driven Methods in Modern Power Engineering)
Multiple event detection and analysis in real time is a challenge for a modern grid as its features are usually non-identifiable. This paper, based on high-dimensional factor models, proposes a data-driven approach to gain insight into the constituent components of a multiple event via the high-resolution phasor measurement unit (PMU) data, such that proper actions can be taken before any sporadic fault escalates to cascading blackouts. Under the framework of random matrix theory, the proposed approach maps the raw data into a high-dimensional space with two parts: (1) factors (spikes, mapping faults); (2) residuals (a bulk, mapping white/non-Gaussian noises or normal fluctuations). As for the factors, we employ their number as a spatial indicator to estimate the number of constituent components in a multiple event. Simultaneously, the autoregressive rate of the noises is utilized to measure the variation of the temporal correlation of the residuals for tracking the system movement. Taking the spatial-temporal correlation into account, this approach allows for detection, decomposition and temporal localization of multiple events. Case studies based on simulated data and real 34-PMU data verify the effectiveness of the proposed approach.
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Keywords:
high-dimensional factor models; large random matrix; multiple event analysis; power systems; spatial-temporal correlation
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MDPI and ACS Style
Yang, F.; Qiu, R.C.; Ling, Z.; He, X.; Yang, H. Detection and Analysis of Multiple Events Based on High-Dimensional Factor Models in Power Grid. Energies 2019, 12, 1360. https://doi.org/10.3390/en12071360
AMA Style
Yang F, Qiu RC, Ling Z, He X, Yang H. Detection and Analysis of Multiple Events Based on High-Dimensional Factor Models in Power Grid. Energies. 2019; 12(7):1360. https://doi.org/10.3390/en12071360
Chicago/Turabian StyleYang, Fan, Robert C. Qiu, Zenan Ling, Xing He, and Haosen Yang. 2019. "Detection and Analysis of Multiple Events Based on High-Dimensional Factor Models in Power Grid" Energies 12, no. 7: 1360. https://doi.org/10.3390/en12071360
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