Big Data-Based Approach to Detect, Locate, and Enhance the Stability of an Unplanned Microgrid Islanding

Huaiguang Jiang, Yan Li, Yingchen Zhang, Jun Jason Zhang, David Wenzhong Gao, Eduard Muljadi, Yi Gu

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10 Scopus citations

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.

Original languageEnglish (US)
Article number04017045
JournalJournal of Energy Engineering
Volume143
Issue number5
DOIs
StatePublished - Oct 1 2017

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Energy Engineering and Power Technology
  • Waste Management and Disposal

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