Distribution Network Reconfiguration using GA & BPSO

P. V. Prasad*, M. Balasubba Reddy**
*_** Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology(A), Hyderabad, India.
Periodicity:November - January'2019
DOI : https://doi.org/10.26634/jps.6.4.15802

Abstract

As increase in the high demand utilization of electrical energy over few decades the power loss issue is persevered. In order to minimize the power losses, various methods are followed in distribution system such as capacitor placement, distribution generator placement and proper conductor selection methods. In all these methods, lot of money is to be invested to decrease the losses. The network reconfiguration method is one, where investment for loss reduction is minimum. By changing the position of sectionalizing and the tie switches the distribution system reconfiguration is done to minimize the power losses. In this paper, Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO) techniques are used for distribution system reconfiguration. The performance of the two algorithms is tested with two different test systems i.e. 33 and 69 node radial distribution systems. The outcomes illustrate that after reconfiguration the power loss is minimized and voltage profile is improved. Finally the results of the two algorithms are compared and found that BPSO has given better results compared to Genetic Algorithm.

Keywords

Distribution Network Reconfiguration, Power loss Reduction, Genetic Algorithm, Binary Particle Swarm Optimization

How to Cite this Article?

Prasad, P.V., and Reddy, M. B. S. (2019). Distribution Network Reconfiguration using GA & BPSO. i-manager’s Journal on Power Systems Engineering, 6(4), 37-44. https://doi.org/10.26634/jps.6.4.15802

References

[1]. Abdelaziz, A. Y., Mohammed, F. M., Mekhamer, S. F., & Badr, M. A. L. (2009). A modified particle swarm optimization technique for distribution systems reconfiguration. The Online Journal on Electronics and Electrical Engineering (OJEEE), 1(2), 121-129.
[2]. Amanulla, B., Chakrabarti, S., & Singh, S. N. (2012). Reconfiguration of power distribution systems considering reliability and power loss. IEEE Transactions on Power Delivery, 27(2), 918-926. https://doi.org/10.1109/TPWRD. 2011.2179950
[3]. Franken, N., & Engelbrecht, A. P. (2005). Particle swarm optimization approaches to coevolve strategies for the iterated prisoner's dilemma. IEEE Transactions on evolutionary computation, 9(6), 562-579. https://doi.org/ 10.1109/TEVC.2005.856202
[4]. Hu, Y., Hua, N., Wang, C., Gong, J., & Li, X. (2010, August). Research on distribution network reconfiguration. In 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, Vol. 1, (pp. 176-180). IEEE. https://doi.org/10.1109/CMCE. 2010.5610464
[5]. Kennedy, D. J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks (pp. 1942-1948).
[6]. Kennedy, J., & Eberhart, R. C. (1997, October). A discrete binary version of the particle swarm algorithm. In 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics And Simulation, Vol. 5, (pp. 4104-4108). IEEE. https://doi.org/ 10.1109/ICSMC.1997.637339.
[7]. Nara, K., Shiose, A., Kitagawa, M., & Ishihara, T. (1992). Implementation of genetic algorithm for distribution systems loss minimum re-configuration. IEEE Transactions on Power Systems, 7(3), 1044-1051. https://doi.org/10. 1109/59.207317
[8]. Rao, P. R., & Sivanagaraju, S. (2010). Radial distribution network reconfiguration for loss reduction and load balancing using plant growth simulation algorithm. International Journal on Electrical Engineering and Informatics, 2(4), 266-277.
[9]. Ravibabu, P., Ramya, M. V. S., Sandeep, R., Karthik, M. V., & Harsha, S. (2010, April). Implementation of improved genetic algorithm in distribution system with feeder reconfiguration to minimize real power losses. In 2010 2nd International Conference on Computer Engineering and Technology, Vol. 4, (pp. V4-320). IEEE. https://doi.org/10. 1109/ICCET.2010.5485563
[10]. Sadri, J., & Suen, C. Y. (2006, July). A genetic binary particle swarm optimization model. In 2006 IEEE International Conference on Evolutionary Computation (pp. 656-663). IEEE. https://doi.org/10.1109/CEC.2006.16 88373
[11]. Shamsudin, N. H., Omar, N. F., Sulaima, M. F., Jaafar, H. I., & Kadir, A. F. A. (2014). The distribution network reconfiguration improved performance of genetic algorithm considering power losses and voltage profile. International Journal of Engineering and Technology, 6(2), 1247-1258.
[12]. Taşgetiren, M. F., & Liang, Y. C. (2003). A binary particle swarm optimization algorithm for lot sizing problem. Journal of Economic and Social Research, 5(2), 1-20.
[13]. Vitorino, R. M., Neves, L. P., & Jorge, H. M. (2009, June). Network reconfiguration to improve reliability and efficiency in distribution systems. In 2009 IEEE Bucharest PowerTech (pp. 1-7). IEEE. https://doi.org/110.1109/ PTC.2009.5281806
[14]. Zhang, C., & Hu, H. (2005, October). Using PSO algorithm to evolve an optimum input subset for a SVM in time series forecasting. In 2005 IEEE International Conference on Systems, Man and Cybernetics, Vol. 4, (pp. 3793-3796). IEEE. https://doi.org/10.1109/ICSMC.2005. 1571737
[15]. Zhang, J., Huang, T., & Zhang, H. (2005). The reactive power optimization of distribution network based on an improved genetic algorithm. In 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific (pp. 1-4). IEEE. https://doi.org/10.1109/TDC. 2005.1546960
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