| Peer-Reviewed

Maximum Power Point Tracking (MPPT) Using Artificial Bee Colony Based Algorithm for Photovoltaic System

Received: 29 December 2015     Accepted: 5 January 2016     Published: 15 January 2016
Views:       Downloads:
Abstract

The Artificial Bee Colony based algorithm (ABC) studied in this paper is assigned as an intelligent control of photovoltaic system. The output power of a photovoltaic panel depends on solar irradiation and temperature. Therefore, it is important to operate the photovoltaic (PV) panel in its maximum power point. In this aim, the ABC consists to track the optimal duty cycle of the electronic converter, in order to lead to the Maximum Power Point (MPP) of the PV system. Moreover, the classical method Perturb and Observe (P&O) [1-2] is studied in the sake of comparison with the ABC method in Matlab/Simulink, by taking into consideration the efficiency, the speed and the robustness performance when the meteorological conditions change.

Published in International Journal of Intelligent Information Systems (Volume 5, Issue 1)
DOI 10.11648/j.ijiis.20160501.11
Page(s) 1-4
Creative Commons

This is an Open Access article, 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 or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Maximum Power Point, Artificial Bee Colony, P&O, Matlab/Simulink

References
[1] E. M. Ahmed and M. Shoyama, “Variable Step Size Maximum Power Point Tracker Using a Single Variable for Stand-alone Battery Storage PV Systems,” Journal of Power Electronics, Vol. 11, No. 2, March 2011, pp. 218-227.
[2] N. Femia, G. Petrone, G. Spagnuolo and M. Vitelli, “Optimization of Perturb and Observe Maximum Power Point Tracking Method,” IEEE TRANSACTIONS ON POWER ELECTRONICS, Vol. 20, No. 4, July 2005, pp. 963-973.
[3] S. Mekhilef, R. Saidur, and A. Safari, “A review on solar energy use in industries,” Renew. Sustain. Energy Rev., vol. 15, no. 4, pp. 1777–1790, May 2011.
[4] S. Mekhilef, A. Safari, W. E. S. Mustaffa, R. Saidur, R. Omar, and M. A. A. Younis, “Solar energy in Malaysia: Current state and prospects, ”Renew. Sustain. Energy Rev., vol. 16, no. 1, pp. 386–396, Jan. 2012.
[5] M. A. Eltawil and Z. Zhao, “MPPT techniques for photovoltaic applications,” Renewable and Sustainable Energy Reviews, Vol. 25, 2013, pp. 793-813.
[6] Salhi, Mohamed, and Rachid El-Bachtri. "Maximum Power Point Tracker using Fuzzy Control for Photovoltaic System." International Journal of Research and Reviews in Electrical and Computer Engineering 1.2 (2011): 69-75.
[7] Guang Yi Cao, « Mathematical Models of Dc-Dc Converters », Journal of Zhejiang University, pp263-270, China, 2009
[8] V. Tereshko, T. Lee, How information mapping patterns determine foraging behaviour of a honeybee colony, Open Syst. Inf. Dyn. 9 (2002) 181–193.
[9] V. Tereshko, A. Loengarov, Collective decision-making in honeybee foraging dynamics, Comput. Inf. Syst. J. 9 (2005) 1–7.
[10] A. S. Oshaba, E. S. Ali and S. M. Abd Elazim "Artificial Bee Colony Algorithm Based Maximum Power Point Tracking in Photovoltaic System" WSEAS TRANSACTIONS on POWER SYSTEMS (2015) 126-127.
Cite This Article
  • APA Style

    Hassan Salmi, Abdelmajid Badri, Mourad Zegrari. (2016). Maximum Power Point Tracking (MPPT) Using Artificial Bee Colony Based Algorithm for Photovoltaic System. International Journal of Intelligent Information Systems, 5(1), 1-4. https://doi.org/10.11648/j.ijiis.20160501.11

    Copy | Download

    ACS Style

    Hassan Salmi; Abdelmajid Badri; Mourad Zegrari. Maximum Power Point Tracking (MPPT) Using Artificial Bee Colony Based Algorithm for Photovoltaic System. Int. J. Intell. Inf. Syst. 2016, 5(1), 1-4. doi: 10.11648/j.ijiis.20160501.11

    Copy | Download

    AMA Style

    Hassan Salmi, Abdelmajid Badri, Mourad Zegrari. Maximum Power Point Tracking (MPPT) Using Artificial Bee Colony Based Algorithm for Photovoltaic System. Int J Intell Inf Syst. 2016;5(1):1-4. doi: 10.11648/j.ijiis.20160501.11

    Copy | Download

  • @article{10.11648/j.ijiis.20160501.11,
      author = {Hassan Salmi and Abdelmajid Badri and Mourad Zegrari},
      title = {Maximum Power Point Tracking (MPPT) Using Artificial Bee Colony Based Algorithm for Photovoltaic System},
      journal = {International Journal of Intelligent Information Systems},
      volume = {5},
      number = {1},
      pages = {1-4},
      doi = {10.11648/j.ijiis.20160501.11},
      url = {https://doi.org/10.11648/j.ijiis.20160501.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20160501.11},
      abstract = {The Artificial Bee Colony based algorithm (ABC) studied in this paper is assigned as an intelligent control of photovoltaic system. The output power of a photovoltaic panel depends on solar irradiation and temperature. Therefore, it is important to operate the photovoltaic (PV) panel in its maximum power point. In this aim, the ABC consists to track the optimal duty cycle of the electronic converter, in order to lead to the Maximum Power Point (MPP) of the PV system. Moreover, the classical method Perturb and Observe (P&O) [1-2] is studied in the sake of comparison with the ABC method in Matlab/Simulink, by taking into consideration the efficiency, the speed and the robustness performance when the meteorological conditions change.},
     year = {2016}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Maximum Power Point Tracking (MPPT) Using Artificial Bee Colony Based Algorithm for Photovoltaic System
    AU  - Hassan Salmi
    AU  - Abdelmajid Badri
    AU  - Mourad Zegrari
    Y1  - 2016/01/15
    PY  - 2016
    N1  - https://doi.org/10.11648/j.ijiis.20160501.11
    DO  - 10.11648/j.ijiis.20160501.11
    T2  - International Journal of Intelligent Information Systems
    JF  - International Journal of Intelligent Information Systems
    JO  - International Journal of Intelligent Information Systems
    SP  - 1
    EP  - 4
    PB  - Science Publishing Group
    SN  - 2328-7683
    UR  - https://doi.org/10.11648/j.ijiis.20160501.11
    AB  - The Artificial Bee Colony based algorithm (ABC) studied in this paper is assigned as an intelligent control of photovoltaic system. The output power of a photovoltaic panel depends on solar irradiation and temperature. Therefore, it is important to operate the photovoltaic (PV) panel in its maximum power point. In this aim, the ABC consists to track the optimal duty cycle of the electronic converter, in order to lead to the Maximum Power Point (MPP) of the PV system. Moreover, the classical method Perturb and Observe (P&O) [1-2] is studied in the sake of comparison with the ABC method in Matlab/Simulink, by taking into consideration the efficiency, the speed and the robustness performance when the meteorological conditions change.
    VL  - 5
    IS  - 1
    ER  - 

    Copy | Download

Author Information
  • EEA&TI Laboratory, Faculty of Sciences and Techniques, Hassan II Casablanca University, Mohammedia, Morocco

  • EEA&TI Laboratory, Faculty of Sciences and Techniques, Hassan II Casablanca University, Mohammedia, Morocco

  • EEA&TI Laboratory, Faculty of Sciences and Techniques, Hassan II Casablanca University, Mohammedia, Morocco

  • Sections