Big Data-Based Approach to Detect, Locate, and Enhance the Stability of an Unplanned Microgrid Islanding
Journal Article
·
· Journal of Energy Engineering
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.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Electricity Delivery and Energy Reliability
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1373679
- Report Number(s):
- NREL/JA--5D00-67982
- Journal Information:
- Journal of Energy Engineering, Journal Name: Journal of Energy Engineering Journal Issue: 5 Vol. 143; ISSN 0733-9402
- Publisher:
- American Society of Civil Engineers (ASCE)
- Country of Publication:
- United States
- Language:
- English
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