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Reconfiguration of the Radial Distribution for Multiple DGs by Using an Improved PSO

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Networking, Intelligent Systems and Security

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 237))

Abstract

PSO is one of the famous algorithms that help to find the global solution; in this study, our main objective is to improve the result found by the PSO algorithm to find the optimal reconfiguration by adjusting the inertia weight parameter. In this paper, I select the chaotic inertia weight parameter and the hybrid strategy using the combination between the chaotic inertia weight and the success rate, these kinds of parameters are chosen due to their accuracy, and they give the best solution compared with other types of parameter. To test the performance of this study, I used the IEEE 33 bus in the case of the presence of the DGs, and a comparative study is done to check the reliability and the quality of these two suggested strategies. In the end, it is noticed that the reconfiguration by using the chaotic inertia weight gives a better result than the hybrid strategy and the other studies: reduce losses, improve the voltage profile at each node, and give the solution at a significant time.

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References

  1. Peng, F.Z.: Editorial Special Issue on Distributed Power Generation. IEEE Trans. Power Electron. 19(5), 2 (2004)

    Google Scholar 

  2. Carreno, E.M., Romero, R., Padilha-Feltrin, A.: An efcient codifcation to solve distribution network reconfguration for loss reduction problem. IEEE Trans. Power Syst. 23(4), 1542–1551 (2008)

    Article  Google Scholar 

  3. Salama, M.M.A., El-Khattam, W.: Distributed generation technologies, defnitions and benefts. Electric Power Systems Research 71(2), 119–128 (2004)

    Article  Google Scholar 

  4. Multon, B.: L’énergie électrique: analyse des resources et de la production.Journées de la section électrotechnique du club EEA (1999)

    Google Scholar 

  5. Strasser, T., Andrén, F., Kathan, J., Cecati, C., Buccella, C., Siano, P., Leitão, P., Zhabelova, G., Vyatkin, V., Vrba, P., Mařík, V.: A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems. IEEE Trans. Industr. Electron. 62(4), 2424–2438 (2014)

    Article  Google Scholar 

  6. Caire, R.: Gestion de la production décentralisée dans les réseaux de distribution.Institut National Polytechnique de Grenoble, tel-00007677 (2004)

    Google Scholar 

  7. Xie, L., Ilic, M.D.: Model predictive dispatch in electric energy systems with intermittent resources. In IEEE International Conference on Systems, Man and Cybernetics (2008)

    Google Scholar 

  8. JUMA, S.A.: Optimal radial distribution network reconfiguration using modified shark smell optimization. (2018) http://hdl.handle.net/123456789/4854

  9. Sivkumar, M.: A Simple Algorithm for Distribution System Load Flow with Distributed Generation, In IEEE International Conference on Recent Advances and Innovations in Engineering, Jaipur, India (2014)

    Google Scholar 

  10. Gallego, LA, Carreno E., Padilha-Feltrin, A.: Distributed generation modeling for unbalanced three-phase power flow calculations in smart grids. In Transmission and Distribution Conference and Exposition: Latin America (T&D-LA) (2010)

    Google Scholar 

  11. Chidanandappa, R., Ananthapadmanabha, T.:Genetic algorithm based network reconfiguration in distribution systems with Multiple DGs for time varying loads. SMART GRID Techno. 21, 460–467 (2015)

    Google Scholar 

  12. Ogunjuyigbe, A., Ayodele, T., Akinola, O.: Impact of distributed generators on the power loss and voltage profile of sub-transmission network. J. Electr. Syst. Inf. Technol. 3, 94–107 (2016)

    Article  Google Scholar 

  13. Ahmad, S., Asar, A.U., Sardar, S., Noor, B.: Impact of distributed generation on the reliability of local distribution system. IJACSA 8(6), 375–382 (2017)

    Article  Google Scholar 

  14. Ma, C., Li, C., Zhang, X., Li, G., Han, Y.: Reconfiguration of distribution networks with distributed generation using a dual hybrid particle swarm optimization algorithm.Hindawi Math. Probl. Eng. 2017, 11 (2017)

    Google Scholar 

  15. Sudhakara Reddy, A.V., Damodar Reddy, M.: “Optimization of network reconfiguration by using particle swarm optimization. In 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) (2016)

    Google Scholar 

  16. Tandon, A., Saxena, D.: Optimal reconfiguration of electrical distribution network using selective particle swarm optimization algorithm. In International Conference on Power, Control and Embedded Systems (2014)

    Google Scholar 

  17. Inji Ibrahim Atteya: Hamdy Ashour, Nagi Fahmi and Danielle Strickland, Radial distribution network reconfiguration for power losses reduction using a modified particle swarm optimisation, . Open Access Proceedings J. 2017(1), 2505–2508 (2017)

    Article  Google Scholar 

  18. Bansal, J.C., Singh, P.K., Saraswat, M., Verma, A., Jadon, S.S., Abraham, A.: Inertia weight strategies in particle swarm optimization. In Third World Congress on Nature and Biologically Inspired Computing (2011).

    Google Scholar 

  19. Arasomwan, A.M., ADEWUMI, A.O.: On adaptive chaotic inertia weights in particle swarm optimization. In IEEE Swarm Intelligence Symposium (2013)

    Google Scholar 

  20. Enacheanu, F.: outils d’aide à la conduite pour les opérateurs des réseaux de distribution (2008).https://tel.archives-ouvertes.fr/tel-00245652

  21. Sharma, A., Saini, M., Ahmed, M.: Power flow analysis using NR method. In International Conference on Innovative Research in Science, Technoloy and Management, Kota, Rajasthan, India (2017)

    Google Scholar 

  22. Baran, M.E., Wu, F.F.: Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Delivery 4(2), 1401–1407 (1989)

    Article  Google Scholar 

  23. Jangjoo, M.A., Seifi, A.R.: Optimal voltage control and loss reduction in microgrid by active and reactive power generation.J. Intell. & Fuzzy Syst., 27, 1649–1658 (2014)

    Google Scholar 

  24. Moarrefi, H., Namatollahi, M., Tadayon, M.: Reconfiguration and distributed Generation(DG) placement considering critical system condition. In 22nd International Conference and Exhibition on Electricity Distribution (2013)

    Google Scholar 

  25. Kennedy, J., Eberhart, R.: Particle swarm optimization. In International Conference on Neural Networks (1995)

    Google Scholar 

  26. M’dioud, M., ELkafazi, I., Bannari, R.: An improved reconfiguration of a radial distribution network by using the minimum spanning tree algorithm. Solid State Technol. 63(6), 9178–9193 (2020)

    Google Scholar 

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Appendix

Appendix

See Tables 4, 5, 6, and 7

Table 4 Set of the loops for IEEE 33 bus [8]
Table 5 Parameters of the proposed PSO
Table 6 Line and load data of IEEE 33 bus [22]
Table 7 Voltage Profile in the case of using the chaotic inertia weights and the combination strategy

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M’dioud, M., Bannari, R., Elkafazi, I. (2022). Reconfiguration of the Radial Distribution for Multiple DGs by Using an Improved PSO. In: Ben Ahmed, M., Teodorescu, HN.L., Mazri, T., Subashini, P., Boudhir, A.A. (eds) Networking, Intelligent Systems and Security. Smart Innovation, Systems and Technologies, vol 237. Springer, Singapore. https://doi.org/10.1007/978-981-16-3637-0_18

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