Abstract
The network reconfiguration for service restoration in distribution systems is a combinatorial complex optimization problem that usually involves multiple non-linear constraints and objectives functions. For large networks, no exact algorithm has found adequate restoration plans in real-time, on the other hand, Multi-objective Evolutionary Algorithms (MOEA) using the Node-depth enconding (MEAN) is able to efficiently generate adequate restorations plans for relatively large distribution systems. An MOEA for the restoration problem should provide restoration plans that satisfy the constraints and reduce the number of switching operations in situations of one fault. For diversity of real-world networks, those goals are met by improving the capacity of the MEAN to explore both the search and objective spaces. This paper proposes a new method called MEA2N with Strength Pareto table (MEA2N-STR) properly designed to restore a feeder fault in networks with significant different bus sizes: 3 860 and 15 440. The metrics R 2, R 3, Hypervolume and ε-indicators were used to measure the quality of the obtained fronts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Toth, P., Vigo, D.: The vehicle routing problem. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (2001)
Coelho, G., Von Zuben, F., da Silva, A.: A Multiobjective Approach to Phylogenetic Trees: Selecting the Most Promising Solutions from the Pareto Front. In: Seventh International Conference on Intelligent Systems Design and Applications, ISDA 2007, pp. 837–842 (October 2007)
Santos, A., Delbem, A., London, J., Bretas, N.: Node-Depth Encoding and Multiobjective Evolutionary Algorithm Applied to Large-Scale Distribution System Reconfiguration. IEEE Transactions on Power Systems 25(3), 1254–1265 (2010)
Martins, J.P., Soares, A.H.M., Vargas, D.V., Delbem, A.C.B.: Multi-objective Phylogenetic Algorithm: Solving Multi-objective Decomposable Deceptive Problems. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds.) EMO 2011. LNCS, vol. 6576, pp. 285–297. Springer, Heidelberg (2011)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical report (2001)
Deb, K., Sundar, J.: Reference point based multi-objective optimization using evolutionary algorithms. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO 2006, pp. 635–642. ACM, New York (2006)
Zhang, Q., Li, H.: MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation 11(6), 712–731 (2007)
Rothlauf, F.: Representations for Genetic and Evolutionary Algorithms. Springer (2006)
de Lima, T.W., Rothlauf, F., Delbem, A.C.: The node-depth encoding: analysis and application to the bounded-diameter minimum spanning tree problem. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO 2008, pp. 969–976. ACM, New York (2008)
Mansour, M., Santos, A., London, J., Delbem, A., Bretas, N.: Node-depth Encoding and Evolutionary Algorithms applied to service restoration in distribution systems. In: Power and Energy Society General Meeting, pp. 1–8. IEEE (2010)
Sanches, D., Mansour, M., London, J., Delbem, A., Santos, A.: Integrating relevant aspects of moeas to solve loss reduction problem in large-scale Distribution Systems. In: PowerTech, 2011 IEEE Trondheim, pp. 1–6 (June 2011)
Diestel, R.: Graph Theory. Third edn. Graduate Texts in Mathematics, vol. 173. Springer, Heidelberg (2005)
Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms,and Applications. Printce Hall, Englewood Cliffs (1993)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press (2001)
Delbem, A.C.B., De Lima, T., Telles, G.P.: Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design. IEEE Transactions on Evolutionary Computation PP(99), 1 (2012)
Deb, K.: Multi-objective optimization using evolutionary altorithms. Wiley, New York (2001)
De Jong, K.: Evolutionary computation: a unified approach, pp. 2245–2258. ACM, New York (2008)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)
Santos, A., Delbem, A., Bretas, N.: A Multiobjective Evolutionary Algorithm with Node-Depth Encoding for Energy Restoration. In: Fourth International Conference on Natural Computation, ICNC 2008, vol. 6, pp. 417–422 (October 2008)
Delbem, A., de Carvalho, A., Bretas, N.: Main chain representation for evolutionary algorithms applied to distribution system reconfiguration. IEEE Transactions on Power Systems 20(1), 425–436 (2005)
Van Veldhuizen, D.A.: Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Wright-Patterson AFB, OH (1999)
Hansen, M., Jaszkiewicz, A.: Evaluating the quality of approximations to the non-dominated set. Technical report, Poznan University of Technology (March 1998)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C., da Fonseca, V.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)
Zitzler, E., Brockhoff, D., Thiele, L.: The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 862–876. Springer, Heidelberg (2007)
While, L., Hingston, P., Barone, L., Huband, S.: A faster algorithm for calculating hypervolume. IEEE Transactions on Evolutionary Computation 10(1), 29–38 (2006)
While, L., Bradstreet, L., Barone, L., Hingston, P.: Heuristics for optimizing the calculation of hypervolume for multi-objective optimization problems. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 3, pp. 2225–2232 (September 2005)
Source Project (2009), http://lcr.icmc.usp.br/colab/browser/Projetos/MEAN
Kuncheva, L.I., Rodríguez, J.J.: An Experimental Study on Rotation Forest Ensembles. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 459–468. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gois, M.M., Sanches, D.S., Martins, J., Junior, J.B.A.L., Delbem, A.C.B. (2013). Multi-Objective Evolutionary Algorithm with Node-Depth Encoding and Strength Pareto for Service Restoration in Large-Scale Distribution Systems. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds) Evolutionary Multi-Criterion Optimization. EMO 2013. Lecture Notes in Computer Science, vol 7811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37140-0_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-37140-0_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37139-4
Online ISBN: 978-3-642-37140-0
eBook Packages: Computer ScienceComputer Science (R0)