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

Hassan Salmi; Abdelmajid Badri; Mourad Zegrari

doi:doi:10.11648/j.ijiis.20160501.11

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

Hassan Salmi, Abdelmajid Badri, Mourad Zegrari

Published in International Journal of Intelligent Information Systems (Volume 5, Issue 1)

Received: 29 December 2015 Accepted: 5 January 2016 Published: 15 January 2016

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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.

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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

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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

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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

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@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}
}

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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  -

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Author Information

Hassan Salmi

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

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

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

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Plain Text BibTeX RIS

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

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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

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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