Abstract
In this paper, a big data-based approach is proposed for the security improvement of an unplanned microgrid islanding (UMI). The proposed approach contains two major steps: the first step is big data analysis of wide-area monitoring to detect a UMI and locate it; the second step is particle swarm optimization (PSO)-based stability enhancement for the UMI. First, an optimal synchrophasor measurement device selection (OSMDS) and matching pursuit decomposition (MPD)-based spatial-temporal analysis approach is proposed to significantly reduce the volume of data while keeping appropriate information from the synchrophasor measurements. Second, a random forest-based ensemble learning approach is trained to detect the UMI. When combined with grid topology, the UMI can be located. Then the stability problem of the UMI is formulated as an optimization problem and the PSO is used to find the optimal operational parameters of the UMI. An eigenvalue-based multiobjective function is proposed, which aims to improve the damping and dynamic characteristics of the UMI. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed approach.
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Acknowledgments
This work was supported by the U.S. Department of Energy under Contract DE-AC36-08-GO28308 with the National Renewable Energy Laboratory.
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Information & Authors
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Published In
Journal of Energy Engineering
Volume 143 • Issue 5 • October 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Nov 22, 2016
Accepted: Mar 23, 2017
Published online: Jun 28, 2017
Published in print: Oct 1, 2017
Discussion open until: Nov 28, 2017
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