Multi-objective genetic algorithms for the reliability analysis and optimization of electrical transmission networks

• Autorzy: Cadini F. , Zio E. , Golea L. R. , Petrescu C. A.
• Języki publikacji: EN

Abstrakty

EN

The results of two applications of multi-objective genetic algorithms to the analysis and optimization of electrical transmission networks are reported to show the potential of these combinational optimization schemes in the treatment of highly interconnected, complex systems. In a first case study, an analysis of the topological structure of an electrical power transmission system of literature is carried out to identify the most important groups of elements of different sizes in the network. The importance is quantified in terms of group closeness centrality. In the second case study, an optimization method is developed for identifying strategies of expansion of an electrical transmission network by addition of new lines of connection. The objective is that of improving the transmission reliability, while maintaining the investment cost limited.

Słowa kluczowe

EN

multi-objective genetic algorithms; critical infrastructures; reliability efficiency; group closeness centrality measure

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2009
• Tom: Vol. 1
• Strony: 87--94
• Opis fizyczny: Bibliogr. 17 poz., rys., tab., wykr.

Twórcy

autor

Cadini F.

• Polytechnic of Milan, Milan, Italy

autor

Zio E.

• Polytechnic of Milan, Milan, Italy

autor

Golea L. R.

• Polytechnic of Milan, Milan, Italy

autor

Petrescu C. A.

• Polytechnic of Milan, Milan, Italy

Bibliografia

• [1] Billinton, R. & Li, W. (1994). Reliability Assessment of Electric Power Systems Using Monte Carlo Methods, 229-308.
• [2] Cadini, F., Zio, E. & Petrescu, C. A. (2008). Using centrality measures to rank the importance of the components of a complex network infrastructure Proceedings of CRITIS’08.
• [3] CNIP’06 (March 2006). Proceedings of the International Workshop on Complex Network and Infrastructure Protection, Rome, Italy, 28-29.
• [4] Everett, M. G. & Borgatti, S. P. (1999). The centrality of groups and classes, Journal of Mathematical Sociology, Vol. 23, N. 3, 181-201.
• [5] Freeman, L. C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks 1, 215-239.
• [6] Goldberg D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Publ. Co.
• [7] Hines, H. & Blumsack, S. (2008). A Centrality Measure for Electrical Networks. Proceedings of the 41st Hawaii International Conference on System Science.
• [8] Koonce, A. M., Apostolakis, G. E. & Cook, B. K. (2007). Bulk power risk analysis: Ranking infrastructure elements according to their risk significance. Int J Electr Power Energ Syst, doi: 10.1016/j.ijepes.2007.06.013.
• [9] Newmann, M. E. J. (2003). The structure in function of complex networks, SIAM Review 45: 2, 167-256.
• [10] Rocco, C. M., Zio, E. & Salazar, D. E. (2007). Multi-objective Evolutionary Optimisation of the Protection of Complex Networks Exposed to Terrorist Hazard. Proceedings of ESREL, Stavanger, Norway, 25-27 June 2007, Volume 1, 899-905.
• [11] Sabidussi, G. (1966). The Centrality Index of a Graph. Psychometrika, 31.
• [12] The IEEE 14 BUS data can be found on: http://www.ee.washington.edu/research/pstca/.
• [13] Vulnerability, Reliability and safety of Complex Networks and Critical Infrastructures, Special Sessions I and II (2007). Proceedings of ESREL, Stavanger, Norway, 25-27 June 2007, Volume 1.
• [14] Wasserman, S. & Faust, K. (1994). Social Networks Analysi. Cambridge U.P., Cambridge, UK.
• [15] Zio, E., Petrescu, C. A. & Sansavini, G. (2008). Vulnerability analysis of a power transmission system. Proceedings of PSAM’09.
• [16] Zio, E. (2007). From Complexity Science to Reliability Efficiency: A New Way of Looking at Complex Network Systems and Critical Infrastructures. Int. J. Critical Infrastructures, Vol. 3, Nos. 3/4, 488-508.
• [17] Zio, E., Baraldi, P. & Pedroni, N. (2009). Optimal power system generation scheduling by multi-objective genetic algorithms with preferences. Reliab. Eng. Sys. Safety, Vol. 94, Issue 2, 432-444.