Home            Contact us            FAQs
    
      Journal Home      |      Aim & Scope     |     Author(s) Information      |      Editorial Board      |      MSP Download Statistics

     Research Journal of Applied Sciences, Engineering and Technology


Implementation of Rank Evolutionary Programming (REP) in 16 kV Feeder Reconfiguration for Convergence Time Improvement

M.F. Sulaima, M.H. Jali, M.K. Nor, Z.H. Bohari and M.N.M. Nasir
Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Malacca, Malaysia
Research Journal of Applied Sciences, Engineering and Technology  2014  18:1933-1938
http://dx.doi.org/10.19026/rjaset.8.1184  |  © The Author(s) 2014
Received: May ‎19, ‎2014  |  Accepted: June ‎18, ‎2014  |  Published: November 15, 2014

Abstract

The increase of energy demand has generated a complexity of electrical network while contributing to the numbers of power losses in the system. This study presents a Distribution Feeder Reconfigurtion (DFR) by using heuristic algorithm which is called as Rank Evolutionary Programming (REP). The main objectives of this study are to improve the computing time and minimize the power losses effectively. The performance of the REP method will be investigated and the impact to the test system IEEE 16-kV distribution network will be analyzed accordingly. The results of this study is hoped to help the engineers in order to secure and increase the efficiency of the real power distribution system in the future.

Keywords:

Convergence time , distribution feeder reconfiguration, power losses, rank evolutionary programming,


References

  1. Aman, M.M., G.B. Jasmon, K. Naidu, A.H.A. Bakar, 2013. Discrete evolutionary programming to solve network reconfiguration problem. Proceeding of IEEE TENCON Spring Conference, pp: 505-509.
    CrossRef    
  2. Fogel, L.J., A.J. Owens and M.J. Walsh, 1966. Artificial Intelligence through Simulated Evolution. John Wiley and Sons, New York.
  3. Hu, Y., N. Hua, C. Wang and J. Gong, 2010. Research on distribution network reconfiguration. Proceeding of International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE, 2010), pp: 176-180.
  4. Liu, C.C., L. Seung J. and K. Vu, 1989. Loss minimization of distribution feeders: Optimality and algorithm. IEEE Tr. Power Deliver., 4(2): 1281-1289.
  5. Milani, A.E. and M.R. Haghifam, 2013. A new probabilistic approach for distribution network reconfiguration: Applicability to real network. Math. Comput. Model., 57: 169-179.
    CrossRef    
  6. Miranda, V., D. Srinivasan and L.M. Proenca, 1998. Evolutionary computation in power systems. Electr. Power Energ. Syst., 20(2): 89-98.
    CrossRef    
  7. Savier, J.S. and D. Das, 2007. Impact of network reconfiguration on loss allocation of radial distribution systems. IEEE T. Power Deliver., 22(4): 2473-2480.
    CrossRef    
  8. Sulaima, F.M., H. Mokhlis and H.I. Jaafar, 2013. A DNR using evolutionary PSO for power loss reduction. J. Telecommun. Electron. Comput. Eng., 5(1).
  9. Sulaima, M.F., S.A. Othman, M.S. Jamri, R. Omar and M. Sulaiman, 2014a. A DNR by using rank evolutionary particle swarm optimization for power loss minimization. Proceeding of 5th International Conference on Intelligent Systems Modelling and Simulation, pp: 417-422.
    CrossRef    
  10. Sulaima, M.F., M.S. Shidan, W.M. Dahalan, H. Mokhlis, M.F. Baharom and H.I. Jaafar, 2014b. A 16kV distribution network reconfiguration by using evolutionary programming for loss minimizing. Int. J. Appl. Eng. Res., 9(10): 1223-1238.
  11. Taleski, R. and D. Rajicic, 2007. Distribution network reconfiguration for energy loss reduction. IEEE T. Power Syst., 12(1): 398-406.
    CrossRef    
  12. Tsai, M.S. and F.Y. Hsu, 2010. Application of grey correlation analysis in evolutionary programming for distribution system feeder reconfiguration. IEEE T. Power Syst., 25(2): 1126-1133.
    CrossRef    Direct Link
  13. Whitley, D., 2001. An overview of evolutionary algorithm: Practical issues and common pitfalls. Inform. Software Tech., 43: 817-831.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Information
   Sales & Services
Home   |  Contact us   |  About us   |  Privacy Policy
Copyright © 2024. MAXWELL Scientific Publication Corp., All rights reserved