Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms

• Published in: Reliability engineering & system safety Vol. 99; pp. 172 - 177
• Main Authors: Zio, E., Golea, L.R., Rocco S., C.M.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-03-2012; Elsevier
• Subjects: Applied sciences; Couples; Decision theory. Utility theory; Electric networks; Electric power; Electrical engineering. Electrical power engineering; Electrical power engineering; Engineering Sciences; Exact sciences and technology; Flows in networks. Combinatorial problems; Genetic algorithm; Genetic algorithms; Joints; Links; Maximization; Multi-objective optimization; Network performance measure; Networks; Operation. Load control. Reliability; Operational research and scientific management; Operational research. Management science; Optimization; Power networks and lines; Power system vulnerability; Reliability theory. Replacement problems; Power system vulnerability; Multi-objective optimization; Network performance measure; Genetic algorithm; Performance evaluation; Network structure; Sensitivity analysis; Network flow; Dependability; Multiobjective programming; Global analysis; Minimization; Vulnerability; Optimization; Transmission networks; Network topology; High voltage; Electrical network
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2011.11.008

In this paper, an analysis of the vulnerability of the Italian high-voltage (380
kV) electrical transmission network (HVIET) is carried out for the identification of the groups of links (or edges, or arcs) most critical considering the network structure and flow. Betweenness centrality and network connection efficiency variations are considered as measures of the importance of the network links. The search of the most critical ones is carried out within a multi-objective optimization problem aimed at the maximization of the importance of the groups and minimization of their dimension. The problem is solved using a genetic algorithm. The analysis is based only on information on the topology of the network and leads to the identification of the most important single component, couples of components, triplets and so forth. The comparison of the results obtained with those reported by previous analyses indicates that the proposed approach provides useful complementary information.