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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Bibliographic Details
Published in:IEEE internet of things journal Vol. 6; no. 3; pp. 5163 - 5171
Main Authors: Gao, Qinghe, Wang, Yawei, Cheng, Xiuzhen, Yu, Jiguo, Chen, Xi, Jing, Tao
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2019.2897434