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2017 | 24 | Special Issue S3 |
Tytuł artykułu

A novel multi-objective discrete particle swarm optimization with elitist perturbation for reconfiguration of ship power system

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A novel multi-objective discrete particle swarm optimization with elitist perturbation strategy (EPSMODPSO) is proposed and applied to solve the reconfiguration problem of shipboard power system(SPS). The new algorithm uses the velocity to decide each particle to move one step toward positive or negative direction to update the position. An elitist perturbation strategy is proposed to improve the local search ability of the algorithm. Reconfiguration model of SPS is established with multiple objectives, and an inherent homogeneity index is adopted as the auxiliary estimating index. Test results of examples show that the proposed EPSMODPSO performs excellent in terms of diversity and convergence of the obtained Pareto optimal front. It is competent to solve network reconfiguration of shipboard power system and other multi-objective discrete optimization problems
Słowa kluczowe
EN
Wydawca
-
Rocznik
Tom
24
Opis fizyczny
p.79-85,fig.,ref.
Twórcy
autor
  • Marine Engineering College, Dalian Maritime University, Dalian 116026, China
autor
  • Marine Engineering College, Dalian Maritime University, Dalian 116026China
autor
  • Information Science and Technology College, Dalian Maritime University, Dalian, China
Bibliografia
  • 1. Z. Wang, L. Xia, Y. J. Wang, 2014. Multiagent and particle swarm optimization for ship integrated power system network reconfiguration. Mathematical Problems in Engineering, 2014(3):1-7.
  • 2. S. Bose, S. Pal, B. Natarajan, et al, 2012. Analysis of optimal reconfiguration of shipboard power systems. IEEE Transactions on Power Systems, 27(1):189-197.
  • 3. Sanjoy Das, Sayak Bose, Siddharth Pal, et al, 2012. Dynamic reconfiguration of shipboard power systems using reinforcement learning. IEEE Transactions on Power Systems, 27(1):189-197.
  • 4. T. Zhao, Y. Zhang, Z.Q.Zhang, 2015. Power system reconstruction based on hierarchical and partitioned restoration. Automation of Electric Power Systems, 39(14):30-67.
  • 5. H. Li, Q. Zhang, 2009. Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II, IEEE Trans. Evol. Comput. 13 (2) 284-302.
  • 6. X. H. Wang, J. J. Li, J. M. Xiao, 2007. Network reconfiguration of the shipboard power system based on gradient discretization method of particle swarm optimization. Transactions of China Electrotechnical Society, 22(12):140-145.
  • 7. J. Huang, X. F. Zhang, Z. H. Ye, 2011. Method of restoration for integrated ship power system based on multi-agent systems. Proceedings of the CSEE, 31(13):71-78.
  • 8. L. J. Yang, J. C. Liu, Z. G. Lu, et al, 2012. Fault restoration of multi-objective distribution system based on multiAgent evolutionary algorithm. Power System Protection and Control, 40(4):54-58.
  • 9. X. X. Yang, X. F. Zhang, Y. Zhang, 2003. The study of network reconfiguration of the shipboard power system based on heuristic genetic algorithm. Proceedings of the CSEE, 23(10):42-46.
  • 10. D. Das, 2006. A fuzzy multiobjective approach for network reconfiguration of distribution systems. IEEE Transactions on Power Delivery, 21(1):202-209.
  • 11. Y. J. Jiang, J. G. Jiang, S. T. Qiao, 2011. Intelligent Service Restoration of Shipboard Power Network Using Nature Multiobjective Evolutionary Algorithm. Proceedings of the CSEE, 31(31):118-124.
  • 12. Z. H. Zhang, N. L. Tai, 2011. Intelligent approach for service restoration of distribution system with distributed generations. Power System Protection and Control, 39(14):79-85.
  • 13. J. Huang, X. F. Zhang, Y. Chen, et al, 2010. Multiobjective optimal model of service restoration for integrated ship power system and its application. Transactions of China Electrotechnical Society, 25(3):130-137.
  • 14. Y. J. Wang, Y. P. Yang, 2009. Particle swarm optimization with preference order ranking for multi-objective optimization. Information Sciences, 179(12):1944-1959.
  • 15. Q. Z. Lin, J. Q. Li, Z. H. Du, et al, 2015. A novel multiobjective particle swarm optimization with multiple search strategies. European Journal of Operational Research, 247(3):732-744.
  • 16. J. L. Wang, L. Xia, Z. G. Wu, 2012. Multiobjective optimal network reconfiguration of shipboard power system based on non-dominated sorting genetic algorithm-II. Power System Technology, 36(11):58-64.
  • 17. W. Q. Sun, C. M. Wang, Y. Zhang, et al, 2014. Analysis and Evaluation on Power System Operation Homogeneity. Transactions of China Electrotechnical Society, 29(4):173-180.
  • 18. M. Nelson, PE. Jordan, 2015. Automatic reconfiguration of a ship’s power system using graph theory principles. IEEE Transactions on industry applications, 51(3):2651-2656.
  • 19. D. Q. Bi, F. Zhang, X. G. Zeng, et al, 2015. Research on fault restoration of shipboard DC zone distribution systems. Power System Protection and Control, 43(19)60-65.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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