Role of Artificial Intelligence in the Reliability Evaluation of Electric Power Systems

Every reliability analysis effort, in some way, involves searching the state space of the system for those states that represent the events of interest, typically failure of the system or a given node to meet the demand. This essentially translates into a search procedure to efficiently identify states to be examined and then using a mechanism to evaluate these states. Traditionally, reliability analysis methods are based either on an implicit or explicit enumeration process or Monte Carlo sampling. More recently, methods based on artificial intelligence have been investigated both as an alternative to Monte Carlo for the search process as well as state evaluation techniques in conjunction with the Monte Carlo methods. This paper will examine the conceptual basis of overall reliability evaluation process and explore the role of artificial intelligence methods in this context. It will also provide some examples of application to the reliability analysis of hybrid systems involving conventional and alternative energy sources.

Anahtar Kelimeler:

Turk. J. Elec. Eng. & Comp. Sci., 16, (2008), 189-200. Full text: pdf Other articles published in the same issue: Turk. J. Elec. Eng. & Comp. Sci., vol.16, iss.3.

Role of Artificial Intelligence in the Reliability Evaluation of Electric Power Systems

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Turk. J. Elec. Eng. & Comp. Sci., 16, (2008), 189-200. Full text: pdf Other articles published in the same issue: Turk. J. Elec. Eng. & Comp. Sci., vol.16, iss.3.,

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C. Singh, R. Billinton, System Reliability Modelling and Evaluation, Hutchinson, UK, 1977.
G. J. Anders, Probability Concepts in Electric Power Systems, Wiley, New York, 1990.
C. Singh, “Reliability Calculations of Large Systems”, Proc. of 1975 Annual Reliability and Maintainability Symposium, Washington, D.C., 1975.
C. Singh, P. Jirutitijaroen, “Monte Carlo Simulation Techniques for Transmission System Reliability Analysis”, A Tutorial Paper presented at IEEE Power Engineering Society General meeting, Tampa, Florida, May, 2007. [5] N. Samaan, C. Singh, “Adequacy Assessment of Power System Generation Using A ModiŞed Simple Genetic Algorithm”, IEEE Transactions on Power Systems, Vol. 17, No 4, November 2002, pp. 974–981
L. F. Wang, C. Singh, “Adequacy assessment of power-generating systems including wind power integration based on ant colony system algorithm”, IEEE Proceedings of Power Tech Conference (PowerTech), Lausanne, Switzerland, July 2007.
IEEE Committee Report, “IEEE reliability test system”, IEEE Transactions on Power Apparatus and Systems, Vol. PAS-98, No. 6, pp. 2047–2054, 1979.
S. Fockens, A. J. M. van Wijk, W. C. Turkenburg, C. Singh, “Reliability analysis of generating systems including intermittent sources”, International Journal of Electric Power & Energy Systems, Vol. 14, No. 1, pp. 2–8, 1992. [9] M. Dorigo, T. Stutzle, Ant Colony Optimization, Cambridge: The MIT Press, 2004.
L. N. de Castro, F. J. Von Zuben, “Learning and optimization using the clonal selection principle”, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 3, June, pp. 239–251, 2002.
J. Kennedy, R. Eberhart, “Particle swarm optimization”, IEEE Proceedings of the International Conference on Neural Networks, Perth, Australia, pp. 1942–1948, 1995.
D. E. Goldberg, Genetic Algorithm in Search, Optimization, and Machine Learning, Reading, MA: Addison- Wesley, 1989.
C. Singh, X. Luo, H. Kim, “Power system adequacy and security calculations using Monte Carlo Simulation incorporating intelligent system methodology”, IEEE Proceedings of the 9th International Conference on Prob- abilistic Methods Applied to Power Systems (PMAPS), Stockholm, Sweden, 2006.
P. Yuanidis, M. A. Styblinski, D. R. Smith, C. Singh, “Reliability Modeling of Flexible Manufacturing Systems”, Microelectronics and Reliability, Vol. 34, No. 7, pp. 1203–1220, 1994.
C. Singh and L. F. Wang, “An alternative to Monte Carlo simulation for system reliability evaluation: search based on artiŞcial intelligence”, Proceedings of the 3rd International Conference on Reliability and Safety Engineering (INCRESE07), Udaipur, India, December, 2007.