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A Novel Two-Stage Multi-Layer Constrained Spectral Clustering Strategy for Intentional Islanding of Power Grids
Intentional islanding can be considered as the last action to prevent power grids from severe blackouts. In this strategy, the endangered network is deliberately decomposed into self-sustained islands to improve the power grid resilience, reliability, and security. In this way, this paper develops a...
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Published in: | IEEE transactions on power delivery 2020-04, Vol.35 (2), p.560-570 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Intentional islanding can be considered as the last action to prevent power grids from severe blackouts. In this strategy, the endangered network is deliberately decomposed into self-sustained islands to improve the power grid resilience, reliability, and security. In this way, this paper develops a novel optimal intentional islanding solution to deal with deliberate physical attacks on the power system. The proposed solution employs a modified multi-layer constrained clustering method based on multi-layer graphs via subspace analysis on Grassmann manifolds clustering. The main objectives are to minimize the active and reactive powers disruptions crossways for decomposed islands, as well as grouping the generators with high-frequency change similarity so that stable operation of each self-supplied grid is ensured. This technique guarantees that each island is only comprised of generators that are synchronized with each other. The proposed multi-layer technique increases the stability of the island by implementing different criteria such as frequency, active power, and reactive power. To better display the improvement, the method is compared with the single layer constrained clustering that can only handle one criterion at a time. The proposed intentional islanding technique is applied to IEEE-9 bus, IEEE-39 bus, and IEEE-118 bus as small, medium, and large-scale networks, respectively. |
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ISSN: | 0885-8977 1937-4208 |
DOI: | 10.1109/TPWRD.2019.2915342 |