Abstract
This paper presents a multi-population hybrid algorithm to solve the switches placement problem in distribution networks considering remote and manual switches. A genetic algorithm in conjunction with local search procedure is used. In the procedure, reliability index, remote–manual controlled switch and investment costs are considered. The problem is formulated as a multi-objective optimization problem to be solved trough of weighted sum method. This method obtains the optimal solution considering a priori articulation of preferences established by the decision maker in terms of an aggregating function which combines individual objective values into a single utility value. A 282-bus test system is presented, and the results are compared to the solution given by other techniques. The results confirm the efficiency of the proposed method which makes it promising to solve complex problems of switches placement in distribution feeders.
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Alves, H. N. & Sousa, R. S. (2014). A multi-population genetic algorithm to solve multi-objective remote switches allocation problem in distribution networks. In 2014 IEEE symposium on proceedings of computational intelligence for engineering solutions (CIES) (pp. 155–164), 9–12 December 2014.
Allan, R. N., Billinton, R. N., & Oliveira, M. F. (1976). An efficient algorithm for deducing the minimal cuts & reliability indices of a generalized network configuration. IEEE Transactions on Reliability, 25(5), 226–233.
Billinton, R. N., & Jonnavithula, S. (1995). A test system for teaching overall power system reliability assessment. IEEE Transactions on Power Systems, 11(4), 1670–1676.
Chen, C. S., Lin, C. H., Chuang, H. J., Li, C. S., Huang, M. Y., & Huang, C. W. (2006). Optimal placement of line switches for distribution automation systems using immune algorithm. IEEE Transactions on Power Systems, 21(3), 1209–1217.
Choy, R., & Edelman, A. (2005). Parallel MATLAB: Doing it Right. Proceedings of the IEEE, 93(2), 331–341.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Chichester, UK: Wiley.
Dezaki, H. H., Abyaneh, H. A., Agheli, A., & Mazlumi, K. (2012). Optimized switch allocation to improve the restoration energy in distribution systems. Journal of Electrical Engineering, 63(1), 47–52.
Falaghi, H., Haghifam, M. R., & Singh, C. (2009). Ant colony optimization-based method for placement of sectionalizing switches in distribution networks using a fuzzy multiobjective approach. IEEE Transactions on Power Delivery, 24(1), 268–276.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley.
Golestani, S., & Tadayon, M. (2011). Optimal switch placement in distribution power system using linear fragmented particle swarm optimization algorithm preprocessed by GA. In 8th international conference on the European Energy Market (EEM) (pp. 537–542).
Huang, M., Liu, P & Liang, X. (2010). An improved multi-population genetic algorithm for job shop scheduling problem. In Proceedings of the 2010 IEEE international conference on progress in informatics and computing (PIC2010) (pp. 272–275).
Levitin, G., Mazal-Tov, S., & Elmakis, D. (1995). Genetic algorithm for optimal sectionalizing in radial distribution systems with alternative supply. Electric Power Systems Research, 35, 149–155.
Li, Y. M., Li, W., Yan, W., & Jia, X. F. (2012). Daily generation scheduling for reducing unit regulating frequency using multi-population genetic algorithm, Power and Energy Society General Meeting. IEEE, 2012, 1–7.
Moradi, A., & Fotuhi-Firuzabad, M. (2008). Optimal switch placement in distribution systems using trinary particle swarm optimization algorithm. Electric Power Systems Research, 23(1), 271–279.
Samsi, S., Gadepally, V., & Krishnamurthy, A. (2010). MATLAB for signal processing on multiprocessors and multicores. Signal Processing Magazine IEEE, 27(2), 40–49.
Sharma, G., & Martin, J. (2009). MATLAB: A language for parallel computing. International Journal of Parallel Programming. doi:10.1007/s10766-008-0082-5.
Sperandio, M., Aranha Neto, E. A. C., Sica, E. T., Trevisan, F., Camargo, C. C. B., Coelho J., & Ramos, R. (2007). Automation planning of loop controlled distribution feeders. In International Conference on Electrical Engineering (CEE-07).
Teng, J., & Lu, C. (2002). Feeder switch relocation for customer interruption costs minimization. IEEE Transactions on Power Delivery, 17(1), 254–259.
The MathWorks. Parallel computing toolbox Documentation, (2009). http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/
Tippachon, W., & Rerkpreedapong, D. (2009). Multiobjective optimal placement of switches and protective devices in electric power distribution systems using ant colony optimization. Electric Power Systems Research, 79(7), 1171–1178.
Toune, S., Fudo, H., Gengi, T., Fukuyama, Y., & Nakanishi, Y. (1998). A reactive Tabu Search for service restoration in electric power distribution systems. In IEEE International conference on evolutionary computation (pp. 1–7), Anchorage, Alaska, 4–11 May.
Ziari, I., Ledwich, G., Wishart, M., Ghosh, A., & Dewadasa, M. (2009). Optimal allocation of a cross-connection and sectionalizers in distribution systems. In TENCON 2009 IEEE Region 10 Conference (pp. 1–5).
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Alves, H.d.N. A Multi-population Hybrid Algorithm to Solve Multi-objective Remote Switches Placement Problem in Distribution Networks. J Control Autom Electr Syst 26, 545–555 (2015). https://doi.org/10.1007/s40313-015-0194-2
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DOI: https://doi.org/10.1007/s40313-015-0194-2