Recently, accurate modeling of the differences between the current and voltage (I–V) characteristics of solar cells has been the main focus of many research studies. Mostly the results were obtained only for single diode or double diode solar cells, not for both or even for photovoltaic (PV) modules. Moreover, the effect of different shading conditions and different temperatures should be considered; otherwise, the obtained results would be reliable for specific weather conditions and unreliable for all real conditions. In this study, a novel nature-inspired optimization method known as the lightning search algorithm (LSA) was developed to extract the parameters of single diode and double diode solar cells as well as for a PV module. LSA is formulated based on lightning, which originates from thunderstorms. Experimental data from multicrystalline KC200GT solar panels were used to test the single diode and double diode solar panel models, and experimental data from the monocrystalline SQ150-PC solar panels were used to test the PV module model. The experimental data are first collected at the same temperature at five different irradiance levels. In the second stage, variations in temperature are considered at the same irradiance level. The extraction results in the LSA I–V curves accurately fit the entire range of the experimental data, while many fluctuations were seen in the particle swarm optimization (PSO) and bee colony optimization (BCO) I–V curves. The convergence characteristics of LSA were also evaluated in terms of accuracy and speed. For all cases, when LSA was used, the accuracies matched well with the entire range of experimental data. In addition, the value of the objective function using LSA was lower, and that method converged much faster than PSO and BCO.

References

References
1.
Subudhi
,
B.
, and
Pradhan
,
R.
,
2011
, “
Characteristics Evaluation and Parameter Extraction of a Solar Array Based on Experimental Analysis
,”
IEEE International Conference on Power Electronics and Drive System
(
PEDS
), Singapore, Dec. 5–8, pp.
340
344
.
2.
AlRashidi
,
M. R.
,
El-Naggar
,
K. M.
, and
AlHajri
,
M. F.
,
2014
, “
Extraction of Photovoltaic Characteristics Using Simulated Annealing
,”
2nd International Conference on Advances in Engineering Sciences and Applied Mathematics
(
ICAESAM′ 2014
), Istanbul, Turkey, May 4–5, pp.
77
79
.
3.
Tamrakar
,
R.
, and
Gupta
,
A.
,
2015
, “
A Review: Extraction of Solar Cell Modelling Parameters
,”
Int. J. Innovative Res. Electr., Electron., Instrum. Control Eng.
,
3
(
1
), pp.
55
60
.
4.
Lingyun
,
X.
,
Lefei
,
S.
,
Wei
,
H.
, and
Cong
,
J.
,
2011
, “
Solar Cells Parameter Extraction Using a Hybrid Genetic Algorithm
,”
Third International Conference on Measuring Technology and Mechatronics Automation
(
ICMTMA
), Shanghai, Jan. 6–7, pp.
306
309
.
5.
Zagrouba
,
M.
,
Sellami
,
A.
,
Bouaicha
,
M.
, and
Ksouri
,
M.
,
2010
, “
Identification of PV Solar Cells and Modules Parameters Using the Genetic Algorithms: Application to Maximum Power Extraction
,”
Sol. Energy
,
48
(
5
), pp.
860
866
.
6.
Askarzadeh
,
A.
, and
Rezazadeh
,
A.
,
2012
, “
Parameter Identification for Solar Cell Models Using Harmony Search-Based Algorithms
,”
Sol. Energy
,
86
(
11
), pp.
3241
3249
.
7.
Qin
,
H.
, and
Kimball
,
J. W.
,
2011
, “
Parameter Determination of Photovoltaic Cells From Field Testing Data Using Particle Swarm Optimization
,”
IEEE Power and Energy Conference at Illinois
(
PECI
), Champaign, IL, Feb. 25–26.
8.
Ye
,
M.
,
Wang
,
X.
, and
Xu
,
Y.
,
2009
, “
Parameter Extraction of Solar Cells Using Particle Swarm Optimization
,”
J. Appl. Phys.
,
105
(
9
), pp.
1
10
.
9.
Saravanan
,
C.
, and
Panneerselvam
,
M. A.
,
2013
, “
A Comprehensive Analysis for Extracting Single Diode PV Model Parameters by Hybrid GA-PSO Algorithm
,”
Int. J. Comput. Appl.
,
78
(
8
), pp.
16
19
.
10.
Huang
,
W.
,
Jiang
,
C.
,
Xue
,
L.
, and
Song
,
D.
,
2011
, “
Extracting Solar Cell Model Parameters Based on Chaos Particle Swarm Algorithm
,”
International Conference on Electric Information and Control Engineering
(
ICEICE
), Wuhan, China, Apr. 15–17, pp.
398
402
.
11.
Oliva
,
D.
,
Cuevas
,
E.
, and
Pajares
,
G.
,
2014
, “
Parameter Identification of Solar Cells Using Artificial Bee Colony Optimization
,”
Energy
,
72
, pp.
93
102
.
12.
Ketkar
,
M.
, and
Chopde
,
M.
,
2014
, “
Efficient Parameter Extraction of Solar Cell Using Modified ABC
,”
Int. J. Comput. Appl.
,
102
(
1
), pp.
1
6
.
13.
Ma
,
J.
,
Ting
,
T. O.
,
Man
,
K. L.
,
Zhang
,
N.
,
Guan
,
S. U.
, and
Wong
,
P. W.
,
2013
, “
Parameter Estimation of Photovoltaic Models Via Cuckoo Search
,”
J. Appl. Math.
,
2013
, p.
362619
.
14.
Jovanovic
,
R.
,
2013
, “
Optimization of Splits Spectrum and Multi-Junction Photovoltaic Cells Using Cuckoo Search Inspired Hybridization of The Nelder-Mead Simplex Algorithm
,” Qatar Foundation Annual Research Conference (
ARC '14
), Doha, Qatar, Nov. 18–19.
15.
Biswas
,
A.
,
Das
,
S.
,
Abraham
,
A.
, and
Dasgupta
,
S.
,
2010
, “
Analysis of the Reproduction Operator in an Artificial Bacterial Foraging System
,”
Appl. Math. Comput.
,
215
(
9
), pp.
3343
3355
.
16.
Han
,
W.
,
Wang
,
H. H.
, and
Chen
,
L.
,
2014
, “
Parameters Identification for Photovoltaic Module Based on an Improved Artificial Fish Swarm Algorithm
,”
Sci. World J.
,
2014
, p.
859239
.
17.
Shareef
,
H.
,
Ibrahim
,
A. A.
, and
Mutlag
,
A. H.
,
2015
, “
Lightning Search Algorithm
,”
Appl. Soft Comput.
,
36
, pp.
315
333
.
18.
Shareef
,
H.
,
Mutlag
,
A. H.
, and
Mohamed
,
A.
,
2015
, “
A Novel Approach for Fuzzy Logic PV Inverter Controller Optimization Using Lightning Search Algorithm
,”
Neurocomputing
,
168
, pp.
435
453
.
19.
Abd Ali
,
J.
,
Hannan
,
M. A.
, and
Mohamed
,
A.
,
2015
, “
A Novel Quantum Behaved Lightning Search Algorithm Approach to Improve the Fuzzy Logic Speed Controller for an Induction Motor Drive
,”
Energies
,
8
(
12
), pp.
13112
13136
.
20.
Abd Ali
,
J.
,
Hannan
,
M. A.
,
Mohamed
,
A.
, and
Abdolrasol
,
M. G. M.
,
2016
, “
Fuzzy Logic Speed Controller Optimization Approach for Induction Motor Drive Using Backtracking Search Algorithm
,”
Measurement
,
78
, pp.
49
62
.
21.
Appelbaum
,
J.
, and
Peled
,
A.
,
2014
, “
Parameters Extraction of Solar Cells—A Comparative Examination of Three Methods
,”
Sol. Energy Mater. Sol. Cells
,
122
, pp.
64
173
.
22.
Yuan
,
X.
,
Xiang
,
Y.
, and
He
,
Y.
,
2014
, “
Parameter Extraction of Solar Cell Models Using Mutative-Scale Parallel Chaos Optimization Algorithm
,”
Sol. Energy
,
108
, pp.
238
251
.
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