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

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.

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability
OSTI Identifier:
1373679
Report Number(s):
NREL/JA-5D00-67982
Journal ID: ISSN 0733-9402
DOE Contract Number:
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Energy Engineering; Journal Volume: 143; Journal Issue: 5
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; smart grid; big data; unplanned microgrid islanding; synchrophasor measurement device; ensemble learning; random forest; particle swarm optimization

Citation Formats

Jiang, Huaiguang, Li, Yan, Zhang, Yingchen, Zhang, Jun Jason, Gao, David Wenzhong, Muljadi, Eduard, and Gu, Yi. Big Data-Based Approach to Detect, Locate, and Enhance the Stability of an Unplanned Microgrid Islanding. United States: N. p., 2017. Web. doi:10.1061/(ASCE)EY.1943-7897.0000473.
Jiang, Huaiguang, Li, Yan, Zhang, Yingchen, Zhang, Jun Jason, Gao, David Wenzhong, Muljadi, Eduard, & Gu, Yi. Big Data-Based Approach to Detect, Locate, and Enhance the Stability of an Unplanned Microgrid Islanding. United States. doi:10.1061/(ASCE)EY.1943-7897.0000473.
Jiang, Huaiguang, Li, Yan, Zhang, Yingchen, Zhang, Jun Jason, Gao, David Wenzhong, Muljadi, Eduard, and Gu, Yi. 2017. "Big Data-Based Approach to Detect, Locate, and Enhance the Stability of an Unplanned Microgrid Islanding". United States. doi:10.1061/(ASCE)EY.1943-7897.0000473.
@article{osti_1373679,
title = {Big Data-Based Approach to Detect, Locate, and Enhance the Stability of an Unplanned Microgrid Islanding},
author = {Jiang, Huaiguang and Li, Yan and Zhang, Yingchen and Zhang, Jun Jason and Gao, David Wenzhong and Muljadi, Eduard and Gu, Yi},
abstractNote = {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.},
doi = {10.1061/(ASCE)EY.1943-7897.0000473},
journal = {Journal of Energy Engineering},
number = 5,
volume = 143,
place = {United States},
year = 2017,
month =
}
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