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Mathematical Problems in Engineering
Volume 2017, Article ID 9067520, 14 pages
https://doi.org/10.1155/2017/9067520
Research Article

Optimal Power Flow Using Gbest-Guided Cuckoo Search Algorithm with Feedback Control Strategy and Constraint Domination Rule

1Key Laboratory of Network Control & Intelligent Instrument, Chongqing University of Posts and Telecommunications, Ministry of Education, Chongqing 400065, China
2Research Center on Complex Power System Analysis and Control, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
3Key Laboratory of Communication Network and Testing Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
4Guodian Enshi Hydropower Development Co., Ltd., Enshi 445000, China
5School of Information Engineering, Hubei University for Nationalities, Enshi 445000, China

Correspondence should be addressed to Gonggui Chen; moc.621@rewopggnehc

Received 24 May 2017; Accepted 5 December 2017; Published 26 December 2017

Academic Editor: Thomas Hanne

Copyright © 2017 Gonggui Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. K. Vaisakh, L. R. Srinivas, and K. Meah, “Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost functions and statistical analysis,” Applied Soft Computing, vol. 13, no. 12, pp. 4579–4593, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. G. G. Chen, L. Liu, P. Song, and Y. Du, “Chaotic improved PSO-based multi-objective optimization for minimization of power losses and L index in power systems,” Energy Conversion and Management, vol. 86, pp. 548–560, 2014. View at Publisher · View at Google Scholar
  3. S. Duman, U. U. Güvenç, Y. Sönmez, and N. Yörükeren, “Optimal power flow using gravitational search algorithm,” Energy Conversion and Management, vol. 59, pp. 86–95, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. H. W. Dommel and W. F. Tinney, “Optimal Power Flow Solutions,” IEEE Transactions on Power Apparatus and Systems, vol. 87, no. 10, pp. 1866–1876, 1968. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Liu-Lin and H. Nai-Shan, “Differentiation coherence algorithm for steady power flow control,” The Open Electrical & Electronic Engineering Journal, vol. 9, no. 1, article no. A107, pp. 107–116, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Habibollahzadeh and A. Semlyen, “Hydrothermal optimal power flow based on a combined linear and nonlinear programming methodology,” IEEE Transactions on Power Systems, vol. 4, no. 2, pp. 530–537, 1989. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Huneault and F. D. Galiana, “A Survey Of The Optimal Power Flow LiteratureA Survey Of The Optimal Power Flow Literature,” IEEE Transactions on Power Systems, vol. 6, no. 2, pp. 762–770, 1991. View at Publisher · View at Google Scholar · View at Scopus
  8. X. Yan and V. H. Quintana, “Improving an interior point based OPF by dynamic adjustments of step sizes and tolerances,” IEEE Transactions on Power Systems, vol. 14, no. 2, pp. 709–717, 1999. View at Google Scholar · View at Scopus
  9. T. Niknam, M. R. Narimani, and R. Azizipanah-Abarghooee, “A new hybrid algorithm for optimal power flow considering prohibited zones and valve point effect,” Energy Conversion and Management, vol. 58, pp. 197–206, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. M. A. Abido, “Optimal power flow using particle swarm optimization,” International Journal of Electrical Power & Energy Systems, vol. 24, no. 7, pp. 563–571, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. M. R. Adaryani and A. Karami, “Artificial bee colony algorithm for solving multi-objective optimal power flow problem,” International Journal of Electrical Power & Energy Systems, vol. 53, no. 1, pp. 219–230, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Bhattacharya and P. K. Chattopadhyay, “Application of biogeography-based optimisation to solve different optimal power flow problems,” IET Generation, Transmission & Distribution, vol. 5, no. 1, pp. 70–80, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Sayah and K. Zehar, “Modified differential evolution algorithm for optimal power flow with non-smooth cost functions,” Energy Conversion and Management, vol. 49, no. 11, pp. 3036–3042, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. H. R. E. H. Bouchekara, A. E. Chaib, M. A. Abido, and R. A. El-Sehiemy, “Optimal power flow using an Improved Colliding Bodies Optimization algorithm,” Applied Soft Computing, vol. 42, pp. 119–131, 2016. View at Publisher · View at Google Scholar · View at Scopus
  15. X. Yang and S. Deb, “Cuckoo search via Lévy flights,” in Proceedings of the World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214, IEEE Publications, USA, 2009.
  16. J. D. Huang, L. Gao, and X. Y. Li, “An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes,” Applied Soft Computing, vol. 36, pp. 349–356, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. M. K. Naik and R. Panda, “A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition,” Applied Soft Computing, vol. 38, pp. 661–675, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. P. Sekhar and S. Mohanty, “An enhanced cuckoo search algorithm based contingency constrained economic load dispatch for security enhancement,” International Journal of Electrical Power & Energy Systems, vol. 75, pp. 303–310, 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Zabaiou, L.-A. Dessaint, and I. Kamwa, “Preventive control approach for voltage stability improvement using voltage stability constrained optimal power flow based on static line voltage stability indices,” IET Generation, Transmission & Distribution, vol. 8, no. 5, pp. 924–934, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. W. Warid, H. Hizam, N. Mariun, and N. Abdul-Wahab, “Optimal Power Flow Using the Jaya Algorithm,” Energies, vol. 9, pp. 678–696, 2016. View at Publisher · View at Google Scholar
  21. A. R. Kumar and L. Premalatha, “Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization,” International Journal of Electrical Power & Energy Systems, vol. 73, article no. 3472, pp. 393–399, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. A.-A. A. Mohamed, Y. S. Mohamed, A. A. M. El-Gaafary, and A. M. Hemeida, “Optimal power flow using moth swarm algorithm,” Electric Power Systems Research, vol. 142, pp. 190–206, 2017. View at Publisher · View at Google Scholar · View at Scopus
  23. R. P. Singh, V. Mukherjee, and S. P. Ghoshal, “Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem,” Applied Soft Computing, vol. 40, pp. 161–177, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Ghasemi, S. Ghavidel, M. Gitizadeh, and E. Akbari, “An improved teaching-learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow,” International Journal of Electrical Power & Energy Systems, vol. 65, pp. 375–384, 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Gitizadeh, S. Ghavidel, and J. Aghaei, “Using SVC to Economically Improve Transient Stability in Long Transmission Lines,” IETE Journal of Research, vol. 60, no. 4, pp. 319–327, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. R. Roy and H. T. Jadhav, “Optimal power flow solution of power system incorporating stochastic wind power using Gbest guided artificial bee colony algorithm,” International Journal of Electrical Power & Energy Systems, vol. 64, pp. 562–578, 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. A. M. Shaheen, R. A. El-Sehiemy, and S. M. Farrag, “Solving multi-objective optimal power flow problem via forced initialised differential evolution algorithm,” IET Generation, Transmission & Distribution, vol. 10, no. 7, pp. 1634–1647, 2016. View at Publisher · View at Google Scholar · View at Scopus
  28. J. Heng, C. Wang, X. Zhao, and J. Wang, “A Hybrid forecasting model based on empirical mode decomposition and the cuckoo search algorithm: a case study for power load,” Mathematical Problems in Engineering, vol. 2016, Article ID 3205396, 28 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. Lin, C. Zhang, and Z. Liang, “Cuckoo search algorithm with hybrid factor using dimensional distance,” Mathematical Problems in Engineering, vol. 2016, Article ID 4839763, 11 pages, 2016. View at Publisher · View at Google Scholar
  30. I. Rechenberg, Evolutionsstrategie: Optimierung Technischer Systeme Nach Prinzipien der Biologischen Evolution, Frommann-Holzboog, Stuttgart, Germany, 1973.
  31. O. Alsac and B. Stott, “Optimal load flow with steady-state security,” IEEE Transactions on Power Apparatus and Systems, vol. 93, no. 3, pp. 745–751, 1974. View at Publisher · View at Google Scholar · View at Scopus
  32. K. Vaisakh and L. R. Srinivas, “Evolving ant direction differential evolution for OPF with non-smooth cost functions,” Engineering Applications of Artificial Intelligence, vol. 24, no. 3, pp. 426–436, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. The IEEE 57-Bus Test System. http://www.ee.washington.edu/research/pstca/pf57/pg_tca57bus.htm.