Application of the Co-Evolutionary Algorithm with Memory at the Population Level for Optimisation of the Operation of Real Electric Power Distribution Networks

Sylwester Filipiak(1*), Jan Stępień(2)

(1) University of Technology Kielce (Poland), Faculty of Electrical Engineering, Automatics and Computer Science, Poland
(2) University of Technology Kielce (Poland), Faculty of Electrical Engineering, Automatics and Computer Science, Poland
(*) Corresponding author

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A solution of the optimisation problem of complex power electric networks post-fault configuration has been proposed. The analysed problem of search for optimal configurations of electric power distribution networks for changing loadings of network elements and for malfunction conditions is a multi-criteria optimisation problem. In this case the sought-after solution is the collection of Pareto-optimal solutions. Scientific methods belonging to the class of artificial intelligence methods (evolutionary algorithms and classifier system) have been used in the paper. Scientific work of the author is presented the co-evolutionary algorithm using memory at the level of organised populations in the form of five subpopulations, the composition of which is changed and organised according to classifying systems’ procedures. The process of creating a collection of classifiers describing the substitute network configuration was performed by the author supported by the theoretical genetic basics of self-teaching system. Cooperation of the evolutionary algorithm with the classifier system enables significant reduction of the classification time, reduces the iterative calculation process on average by 40 %. The calculations performed for the mapped real system of the medium voltage municipal distribution network have given satisfactory results, confirming the adequate direction of the research. The method presented in the article enables effective search of optimal configurations of distribution networks for various network loadings and also network malfunction conditions
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Evolutionary Algorithms; Distribution Power Networks; Optimization

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Duque, O., Morinigo, D., del Alamo, J. L., Tabu search based algorithm for the multi-criteria optimisation of service restoration in electrical distribution networks, (2007) International Review of Electrical Engineering (IREE), 2 (1), pp. 5-13.

Morelato A. L., Monticelli A. J.: Heuristic search approach to distribution system restoration. IEEE Trans. Power Delivery, vol. 4, Oct. 1989, pp. 2235-2241.

Jazebi, S., Jazebi, S., Rashidinejad, M., Application of a novel real genetic algorithm to accelerate the distribution network reconfiguration, (2009) International Review of Electrical Engineering (IREE), 4 (1), pp. 114-121.

Shirmohammadi D.: Service restoration in distribution networks via network reconfiguration. IEEE Trans. Power Delivery, vol. 7, Apr. 1992, pp. 952-958.

Butler K. L., Sarma N. D. R., Prasad R.: Network reconfiguration for service restoration in shipboard power distribution systems. IEEE Trans. Power Systems, vol. 16, Nov. 2001, pp. 653-661.

Hsiao Y., Chien C.: Enhancement of restoration service in distribution systems using a combination fuzzy-GA method. IEEE Trans. Power Systems, vol. 15, Nov. 2000, pp. 1394-1400.

Toune S., Fudo H., Genji T., Fukuyama Y.: Comparative study of modern heuristic algorithms to service restoration in distribution systems. IEEE Trans. Power Delivery, vol. 17, Jan. 2002, pp. 173-181.

Chao-Shun C., Lin C-H., Hung-Ying T.: A rule-based expert system with colored petri net models for distribution system service restoration. IEEE Trans. Power Systems, vol. 17, Nov. 2002, pp. 1073-1080.

Khushalani S., Solanki, J.M., Schulz, N.N. Optimized Restoration of Unbalanced Distribution Systems. IEEE Transactions on Power Systems, no. 22, Issue 2. 2007, p. 624-630.

Kumar Y., Das, B., Sharma, J. Multiobjective, Multiconstraint Service Restoration of Electric Power Distribution System With Priority Customers. IEEE Transactions on Power Delivery, no. 23, Issue 1, 2008, p. 261-270.

Delbem A. C. B., Carvalho A. C. P. L. F., Bretas N. G.: Main chain representation for evolutionary algorithms applied to distribution system reconfiguration. IEEE Trans. Power Systems., vol. 20, no. 1, Feb. 2005, pp. 425-436.

Dag, G.O., Bagriyanik, M., Controlling unscheduled flows using fuzzy set theory and genetic algorithms, (2010) International Review of Electrical Engineering (IREE), 5 (1), pp. 185-193.

Niknam T., Farsani E. A., Nayeripour M., Firouzi B. B.: Hybrid fuzzy adaptive particle swarm optimization and differential evolution algorithm for distribution feeder reconfiguration. Electric Power Components and Systems, vol. 39, Issue 2, 2011, p. 158 – 175.

Hong Y. Y., Ho S. Y.: Determination of network configuration considering multiobjective in distribution systems using genetic algorithms. IEEE Trans. Power Systems, vol. 20, no. 2, May 2005, pp. 1062-1069.

Stępień J., Madej Z.: Evaluation of structural redundancy efects in medium voltage cable networks., Rynek Energii Issue: 4 Pages: 55-60 Published: AUG 2009.

Stępień J.: Changes in demand structure of energy carriers with the use of waste heat and renewable energy. Rynek Energii Issue: 5 p. 58-62 Published: OCT 2008.

Stępień J.: Complete reliability model of 110/115 kV main supplying points. Przegląd Elektrotechniczny Volume: 84 Issue: 4 p. 128-131 Published: 2008.

Stępień J.: Forecast of heat demands in the low urbanized areas, Rynek Energii, Issue: 6 p. 7-12 Published: DEC 2007.

Filipiak S.: Application of Evolutionary Algorithm in Optimisation of Medium-Voltage Distribution Networks Post-Fault Configuration., International Journal of Electrical Power & Energy Systems Volume 44, Issue 1, January 2013, p. 666–671.

Filipiak, S., Application of evolutionary algorithm and classifier system in optimisation of electric power distribution networks post-fault configuration, (2010) International Review of Electrical Engineering (IREE), 5 (3), pp. 1151-1158.


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