Global Best Algorithm Based Parameter Identification of Solar Cell Models
DOI:
https://doi.org/10.18201/ijisae.2017533892Keywords:
I-V characteristic, Optimization algorithm, Parameter identification, Solar cell modellingAbstract
Effectivity of the solar energy systems is thoroughly dependent of successful modeling of the I-V characteristic curves. However, due to the lack of information about the precise model parameters those are profoundly involved in characterizing governing equations; an efficient design has not been accurately accomplished by researchers yet. This article proposes Global Best Algorithm (GBEST) in order to extract unknown parameters of solar cell models accurately. In order to test the performance of the proposed optimizer, nine different unconstrained optimization test functions are evaluated and their statistical results are compared with the recently developed metaheuristic algorithms. GBEST is applied on PV module, single and double diode models which are mathematically formulated as multi-dimensional and highly nonlinear in their nature. Results reveal that GBEST is superior to the other methods in terms of solution accuracy and efficiency.Downloads
References
D. Oliva, E. Cuevas, and G. Pajares, “Parameter identification of solar cells using artificial bee colony optimization,” Energy, vol.72, pp.93-102, 2014
X. Yuan, Y. Xiang, and Y. He, “Parameter extraction of solar cell models using mutative – scale parallel chaos optimization algorithm,” Sol Energy, vol.108, pp.238 – 251, 2014.
A. Askarzadeh, and A. Rezazadeh, “Extraction of maximum power point in solar cells using bird mating optimizer-based parameters identification approach,” Sol Energy, vol.90, pp.123–133, 2013.
F. Bonanno, G. Capizzi, G. Graditi, C. Napoli, and G.M. Tina, “A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module,” Appl Energy, vol.97, pp.956-961, 2012.
L. Sandrolini, M. Artioli, and U. Reggiani, “Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis,” Appl Energy, vol.87, pp.442-451, 2010.
B. Amrouche, A. Guessoum, and M. Belhamel, “A simple behavioral model for solar module electric characteristics based on first order system step response for MMPT study and comparison,” Appl Energy, vol.91, pp.395-404, 2012.
W. Xiao, M.G.J. Lind, W.G. Dunford, and A. Capel, “Real time identification of optimal operating points in photovoltaic power systems” IEEE Trans Ind Electron, vol.53, pp.1017-1026, 2006.
M. Chegaar, Z. Ouennough, F. Guechi, and H. Langueur, “Determination of solar cells parameters under illuminated conditions,” J Electron Devices, vol.2, pp.17-21, 2003.
M.R. Al-Rashidi, K.M. El-Naggar, and Al-Hajiri MF, “Parameters estimation of double diode solar cell model,” World Acad Sci Eng Technol, vol. 7, pp.93 – 96, 2013.
A. Askarzadeh, and A.Rezazadeh, “Artificial bee swarm optimization algorithm for parameters identification of solar cell models,” Appl Energy, vol.102, pp. 943-949, 2013.
T. Easwarakhanthan, J. Bottin, I. Bouhouch, and C. Boutrit, “Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers,” Sol Energy, vol.4, pp.1-12, 1986.
A. Ortiz-Conde, F.J. Garcia Sanchez, and J. Muci, “New method to extract the model parameters of solar cells from the explicit analytic solutions of their illuminated I-V characteristics,” Solar Energ Mat Sol C, vol.90, pp.352-361, 2006.
H, Saleem, and S. Karmalkar, “An analytical method to extract the physical parameters of a solar cell from four points on the illuminated J-V curve,” IEEE. Electr Device L, vol.30, pp.349-352, 2009.
D.S.H. Chan, J.R. Phillips, and J.C.H. Phang, “A comparative study of extraction methods for solar cell parameters,” Solid State Electron, vol.29, pp.329-337, 1986.
J.A. Jervase, H. Bourdoucen, and A. Al-Lawati, “Solar cell parameter extraction using genetic algorithms,” Meas Sci Technol, vol.12, pp.1922-1925, 2001.
M. Ye, X. Wang, Y. Xu, “Parameter extraction of solar cells using particle swarm optimization,” J Appl Phys, vol.105, 094502-094508, 2009.
K.M. El-Naggar, M.R. AlRashidi, M.F. AlHajiri, and A.K. Al-Othman, “Simulated annealing algorithm for photovoltaic parameters identification,” Sol Energy, vol.86, pp. 266 – 274, 2012.
M. Gomez, A. Lopez, and F. Jurado, “Optimal placement and sizing from standpoint of the investor of photovoltaics grid-connected systems using binary particle swarm optimization,” Appl Energy, vol.87, pp.1911-1918, 2010.
C.R.S. Reinoso, M. Cutrera, M. Battioni, D.H. Milone, and R.H. Buitrago, “Photovoltaic generation model as a function of weather variables using artificial intelligence techniques,” Int J Hydrogen Energ, vol.37, pp.14781 – 14785, 2012.
O. Ekren, and B.Y. Ekren, “Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing,” Appl Energy, vol. 87, pp. 592-598, 2010.
T. Nikham, S.I. Taheri, J. Aghaei, S. Tabatabaei, and M. Nayeripour, “A modified honey bee mating optimization algorithm for multi-objective placement of renewable energy sources,” Appl Energy, vol.88, pp.4817-4830, 2011.
Q. Niu, H. Zhang, and K. Li, “An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models,” Int J Hydrogen Energ, vol.39, pp.3837-3854, 2014.
M, Wolf, G.T. Noel, and R.J. Stirm, “Investigation of the double exponential in the current-voltage characteristics of silicon solar cells,” IEEE T Electron Dev, vol.24, pp.419-428, 1977.
M.R. AlRashidi, M.F. AlHajri, K.M. El-Neggar, and A.K. Al-Othman, “A new estimation approach for determining the I-V characteristics of solar cells,” Sol Energy, vol.85, vol.1543 – 1550, 2011.
O.E. Turgut, and M.T. Coban, “Thermal design of spiral heat exchangers and heat pipes through global best algorithm,” Heat Mass Transf, (2016) (In Press) doi: 10.1007/s00231-016-1861-y.
R. Storn, and K. Price, “Differential evolution – a simple and efficient heuristic for global optimization over continuous space,” J Global Optim, vol.11, pp.341 – 359, 1997.
P. Civicioglu, “Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm,” Comput Geosci, vol.46 pp.229 – 24, 2012.
R. May, “Simple mathematical models with very complicated dynamics,” Nature, vol.261, pp.459 – 467, 1976.
S.Z. Zhao, P.N. Suganthan, Q.K. Pan, and M.F. Tasgetiren, “Dynamic multi swarm particle swarm optimizer with harmony search,” Expert Syst Appl vol.38, pp. 375 – 3742, 2011.
J. Zhang, and X. Ding, “A multi swarm self-adaptive and cooperative particle swarm optimization,” Eng Appl Artif Intel, vol. 24, pp.958 – 967, 2011.
P. Novoa-Hernandez, C. Cruz Corona, and D.A. Pelta, “ Self-adaptive, multipopulation differential evolution in dynamic environments,” Soft Comput, vol.17, pp.1861 – 1881, 2013.
J. Liang, and P.N. Suganthan, “Dynamic multi swarm particle swarm optimizer,” In: Proceedings 2005 IEEE, Swarm Intelligence Symposium, IEEE, 2005. pp. 124-129, 2005.
R. Storn, “On the usage of differential evolution for function optimization,” In: Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), IEEE, Berkeley, 1996, pp.519 – 523, 1996.
R. Gamperle, S.D. Müller, and P. Koumoutsakos, “A parameter study for differential evolution,” In: Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, WSEAS Press, Interlaken, Switzerland, 2002, pp. 293 – 298, 2002.
K. Rönkkönen, S. Kukkonen, and K.V. Price, “Real parameter optimization with differential evolution,” In: IEEE Congress on Evolutionary Computation, 2005, pp. 506 - 513, 2005.
A.K. Qin, V.L. Huang, and P.N. Suganthan, “Differential evolution algorithm with strategy adaptation for global numerical optimization,” IEEE Trans Evol Comput, vol.13, pp.398 – 417, 2009.
W. Gong, A. Fialho, Z. Cai, and H. Li, “Adaptive strategy selection in differential evolution for numerical optimization: An empirical study,” Inform Sciences, vol.181, pp.5364 – 5386, 2011.
G. Jia, Y. Wang, Z. Cai, and Y. Jin, “An improved (µ+ƛ) – constrained differential evolution for constrained optimization,” Inform Sciences, vol.222, pp. 302-322, 2013.
J. Brest, S. Greiner, B. Boscovic, M. Mernik, and V. Zumer, “Self-adaptive control parameters in differential evolution: a comparative study on numerical benchmark problems,” IEEE Trans Evol Comput, vol.10, pp.646 – 657, 2006.
A.H. Gandomi, and X.S. Yang, “Evolutionary boundary constraint handling scheme,” Neural Comput Appl, vol.21, pp.1449 – 1462, 2012.
J. Sun, B. Feng, and W.B. Xu, “Particle swarm optimization with particles having quantum behavior,” In: IEEE 2004 Proceedings of the IEEE Congress on Evolutionary Computation ,Portland, OR, USA, 2004, pp. 325–331, 2004.
P. Yadav, R. Kumar, S.K. Panda, and C.S. Chang, “An intelligent tuned harmony search algorithm for optimization,” Inform Sciences, vol.196, pp.47 – 72, 2012.
O.K. Erol, and I. Eksin, “A new optimization method: Big Bang – Big Crunch,” Adv Eng Softw, vol.37, pp.106 – 111, 2006.
A. Askerzadeh, “Bird mating optimizer: An optimization algorithm inspired by bird mating strategies,” Commun Nonlinear Sci Numer Simul, vol. 19, pp.1213 -1228, 2014.
X.S. Yang, “A new metaheuristic Bat-inspired algorithm,” In: Nature Inspired Cooperative Strategies Optimization (Eds. J.R Gonzales et al.) Studies in Computational Intelligence, Springer Berlin, 284, Springer, 2010, pp. 65 – 74, 2010.
P. Civicioglu, “Backtracking search optimization algorithm for numerical optimization problems,” Appl Math Comput, vol.219, pp.8121 – 8144, 2013.
S. Mirjalili, S.M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv Eng Softw, vol.69, pp.46-61, 2014.
S. Mirjalili, S.M. Mirjalili, and A. Hatamlou, “Multi-Verse Optimizer: a nature-inspired algorithm for global optimization,” Neural Comput Appl, vol.27, pp.495-513, 2016.
A. Askarzadeh, and A. Rezazadeh, “Parameter identification for solar cell models using harmony search-based algorithms,” Sol Energy, vol.86, pp.3241–3249, 2012.
H. Wei, J. Cong, X. Lingyun, and S. Deyun, “Extracting solar cell model parameters based on chaos particle swarm algorithm,” In: IEEE 2011 International Conference on Electric Information and Control Engineering (ICEICE), Wuhan, China, 2011, pp. 398–402, 2011.
M.F. AlHajri, K.M. El-Naggar, M.R. AlRashidi, and A.K. Al-Othman, “Optimal extraction of solar cell parameters using pattern search,” Renew Energ, vol. 44, pp.238–245, 2012.
L.L. Jiang, D.L. Maskell, and J.C. Patra, “Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm,” Appl Energy. vol.112, pp.185 -193, 2013.
A. Askerzadeh, and L.d.S Coelho,“Determination of photovoltaic modules parameters at different operating conditions using a novel bird mating optimizer approach,” Energ Convers Manage, vol.89 pp.608 – 614, 2015.
Downloads
Published
How to Cite
Issue
Section
License
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
