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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Veröffentlicht in:IEEE internet of things journal Jg. 6; H. 3; S. 5163 - 5171
Hauptverfasser: Gao, Qinghe, Wang, Yawei, Cheng, Xiuzhen, Yu, Jiguo, Chen, Xi, Jing, Tao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Abstract 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.
AbstractList 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.
Author Chen, Xi
Jing, Tao
Wang, Yawei
Gao, Qinghe
Yu, Jiguo
Cheng, Xiuzhen
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  organization: School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China
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SubjectTerms 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
Title Identification of Vulnerable Lines in Smart Grid Systems Based on Affinity Propagation Clustering
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