Artificial bee swarm optimization algorithm for parameters identification of solar cell models

Applied Energy

Volumn 102, Issue , 2013, Pages 943-949

  Askarzadeh, Alireza ^a   Rezazadeh, Alireza ^a  

^a SHAHID BEHESHTI UNIVERSITY   (Iran)

Author keywords

Artificial bee swarm optimization; Mathematical modeling; Parameter identification

Indexed keywords

ALGORITHMS; IDENTIFICATION (CONTROL SYSTEMS); MATHEMATICAL MODELS;

CELL MODEL; HONEY BEE; INTELLIGENT BEHAVIOR; META HEURISTIC ALGORITHM; OPTIMIZATION METHOD; PARAMETERS IDENTIFICATION; SOLAR CELL SYSTEM; SWARM OPTIMIZATION; SWARM OPTIMIZATION ALGORITHMS;

SILICON SOLAR CELLS;

ALGORITHM; NUMERICAL METHOD; NUMERICAL MODEL; OPTIMIZATION; PARAMETERIZATION; PHOTOVOLTAIC SYSTEM; SILICON; SOLAR POWER; FUEL CELL; RENEWABLE RESOURCE;

APIS MELLIFERA; APOIDEA;

EID: 84870767174     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2012.09.052     Document Type: Article

References (27)

1. Ishaque K., Salam Z., Mekhilef S., Shamsudin A. Parameter extraction of solar photovoltaic modules using penalty-based differential evolution. Appl Energy 2012, 99:297-308.
2. Orioli A., Gangi A.D. A procedure to calculate the five-parameter model of crystalline silicon photovoltaic modules on the basis of the tabular performance data. Appl Energy 2013, 102:1160-1177.
3. Sandrolini L., Artioli M., Reggiani U. Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis. Appl Energy 2010, 87:442-451.
4. Amrouche B., Guessoum A., Belhamel M. A simple behavioural model for solar module electric characteristics based on the first order system step response for MPPT study and comparison. Appl Energy 2012, 91:395-404.
5. Bonanno F., Capizzi G., Graditi G., Napoli C., Tina G.M. A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module. Appl Energy 2012, 97:956-961.
6. Easwarakhanthan T., Bottin J., Bouhouch I., Boutrit C. Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. Sol Energy 1986, 4:1-12.
7. Chan D.S.H., Phillips J.R., Phang J.C.H. A comparative study of extraction methods for solar cell model parameters. Solid-State Electron 1986, 29:329-337.
8. Jian A., Kapoor A. Exact analytical solutions of the parameters of real solar cells using Lambert W-function. Sol Energy Mater Sol Cells 2004, 81:269-277.
9. Saleem H., Karmalkar S. An analytical method to extract the physical parameters of a solar cell from four points on the illuminated J-V curve. IEEE Electron Device Lett 2009, 30:349-352.
10. AlRashidi M.R., AlHajri M.F., El-Naggar K.M., Al-Othman A.K. A new estimation approach for determining the I-V characteristics of solar cells. Sol Energy 2011, 85:1543-1550.
11. Wei H, Cong J, Lingyun X, Deyun S. Extracting solar cell model parameters based on chaos particle swarm algorithm. In: International conference on electric information and control engineering (ICEICE); 2011. p. 398-402.
12. El-Naggar K.M., AlRashidi M.R., AlHajri M.F., Al-Othman A.K. Simulated annealing algorithm for photovoltaic parameters identification. Sol Energy 2012, 86:266-274.
13. Gomez M., Lopez A., Jurado F. Optimal placement and sizing from standpoint of the investor of photovoltaics grid-connected systems using binary particle swarm optimization. Appl Energy 2010, 87:1911-1918.
14. Reinoso C.R.S., Cutrera M., Battioni M., Milone D.H., Buitrago R.H. Photovoltaic generation model as a function of weather variables using artificial intelligence techniques. Int J Hyrogen Energy 2012, 10.1016/j.ijhydene.2011.12.081.
15. Ekren O., Ekren B.Y. Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing. Appl Energy 2010, 87:592-598.
16. Niknam T., Taheri S.I., Aghaei J., Tabatabaei S., Nayeripour M. A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources. Appl Energy 2011, 88:4817-4830.
17. Dai C., Chen W., Zhu Y. Seeker optimization algorithm for digital IIR filter design. IEEE Trans Ind Electron 2010, 57:1710-1718.
18. Samanta S., Chakraborty S. Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Eng Appl Artif Intell 2011, 24:946-957.
19. Lien L.C., Cheng M.Y. A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization. Expert Syst Appl 2012, 39:9642-9650.
20. Askarzadeh A., Rezazadeh A. A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters. J Zhejiang Univ Sci C 2011, 12:638-646.
21. Akbari R., Mohammadi A., Ziarati K. A novel bee swarm optimization algorithm for numerical function optimization. Commun Nonlinear Sci Numer Simulat 2010, 15:3142-3155.
22. Yang X.S. Engineering optimizations via nature-inspired virtual bee algorithms. Lect Notes Comput Sci 2005, 3562:317-323.
23. Karaboga D., Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 2007, 39:459-471.
24. Wolf M., Noel G., Stirm R. Investigation of the double exponential in the current-voltage characteristics of silicon solar cells. IEEE Trans Ind Electron 1977, 53:1017-1026.
25. AlHajri M.F., El-Naggar K.M., AlRashidi M.R., Al-Othman A.K. Optimal extraction of solar cell parameters using pattern search. Renew Energy 2012, 44:238-245.
26. Askarzadeh A., Rezazadeh A. A grouping-based global harmony search algorithm for modeling of proton exchange membrane fuel cell. Int J Hydrogen Energy 2012, 36:5047-5053.
27. Askarzadeh A., Rezazadeh A. An Innovative global harmony search algorithm for parameter identification of a PEM fuel cell model. IEEE Trans Ind Electron 2012, 59:3473-3480.