Identification of Vulnerable Lines in Smart Grid Systems Based on Affinity Propagation Clustering

In smart grid systems, vulnerable lines may lead to cascading failures which can cause large-scale blackouts. Successfully detecting vulnerable lines can increase the stability of the smart grid systems and reduce the risk of cascading failures. By modeling a smart grid system into a directed graph,...

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Vydané v:IEEE internet of things journal Ročník 6; číslo 3; s. 5163 - 5171
Hlavní autori: Gao, Qinghe, Wang, Yawei, Cheng, Xiuzhen, Yu, Jiguo, Chen, Xi, Jing, Tao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Affinity
Affinity propagation (AP)
Algorithms
cascading failure
Clustering
Clustering algorithms
Complex networks
Electrical properties
factor graph
Failure analysis
Graph theory
Internet of Things
machine learning
max-sum algorithm
Parameter identification
Partitioning algorithms
Power system faults
Power system protection
Propagation
Smart grid
Smart grids
ISSN:2327-4662, 2327-4662
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Shrnutí:In smart grid systems, vulnerable lines may lead to cascading failures which can cause large-scale blackouts. Successfully detecting vulnerable lines can increase the stability of the smart grid systems and reduce the risk of cascading failures. By modeling a smart grid system into a directed graph, we investigate the problem of vulnerable line identification from a clustering perspective. By jointly considering the topological parameters and the electrical properties, we propose an affinity propagation-based bus clustering algorithm to classify buses into clusters, where the center of each cluster represents the most influential bus in each partition. According to the clustering results, we design a vulnerable line identification scheme, which captures different types of potential critical lines in the smart grid system. Experiments over the IEEE-39 bus system demonstrate the effectiveness and correctness of our proposed algorithm.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2019.2897434