Abstract

Maximum power point tracking (MPPT) controllers play an important role in improving the efficiency of solar photovoltaic (SPV) modules. These controllers achieve maximum power transfer from PV modules through impedance matching between the PV modules and the load connected. Several MPPT techniques have been proposed for searching the optimal matching between the PV module and load resistance. These techniques vary in complexity, tracking speed, cost, accuracy, sensor, and hardware requirements. This paper presents the design and modeling of the adaptive neuro-fuzzy inference system (ANFIS)-based MPPT controller. The design consists of a PV module, ANFIS reference model, DC–DC boost converter, and the fuzzy logic (FL) power controller for generating the control signal for the converter. The performance of the proposed ANFIS-based MPPT controller is evaluated through simulations in the matlab/simulink environment. The simulation results demonstrated the effectiveness of the proposed technique since the controller can extract the maximum available power for both steady-state and varying weather conditions. Moreover, a comparative study between the proposed ANFIS-based MPPT controller and the commonly used, perturbation and observation (P&O) MPPT technique is presented. The simulation results reveal that the proposed ANFIS-based MPPT controller is more efficient than the P&O method since it shows a better dynamic response with few oscillations about the maximum power point (MPP). In addition, the proposed FL power controller for generating the duty cycle of the DC–DC boost converter also gave satisfying results for MPPT.

References

References
1.
Owusu
,
P. A.
, and
Asumadu-Sarkodie
,
S.
,
2016
, “
A Review of Renewable Energy Sources, Sustainability Issues and Climate Change Mitigation
,”
Cogent Eng.
,
3
(
1
), pp.
1
14
. 10.1080/23311916.2016.1167990
2.
I.-I. R. E. Agency
. “
Renewables Account for Almost Three Quarters of New Capacity in 2019
,” IRENA, https://www.irena.org/newsroom/pressreleases/2020/Apr/Renewables-Account-for-Almost-Three-Quarters-of-New-Capacity-in-2019, Accessed 2020.
3.
Ahmadi
,
M. H.
,
Ghazvini
,
M.
,
Sadeghzadeh
,
M.
,
Nazari
,
M. A.
,
Kumar
,
R.
,
Naeimi
,
A.
, and
Ming
,
T.
,
2018
, “
Solar Power Technology for Electricity Generation: A Critical Review
,”
Energy Sci. Eng.
,
6
(
5
), pp.
340
361
. 10.1002/ese3.239
4.
Malinowski
,
M.
,
Leon
,
J.
, and
Abu-Rub
,
H.
,
2017
, “
Solar Photovoltaic and Thermal Energy Systems: Current Technology and Future Trends
,”
Proc. IEEE
,
105
(
11
), pp.
1
15
. 10.1109/JPROC.2017.2690343
5.
Hossain
,
J.
, and
Mahmud
,
A.
,
2014
,
Renewable Energy Integration: Challenges and Solutions
,
Springer Science & Business Media
,
New York
, p.
1
.
6.
Rosu-Hamzescu
,
M.
, and
Oprea
,
S.
,
2013
, “
Practical Guide to Implementing Solar Panel MPPT Algorithms
,”
Microchip Technology Inc.
7.
Sharma
,
D.
, and
Purohit
,
G.
,
2012
, “
Advanced Perturbation and Observation (P&O) Based Maximum Power Point Tracking (MPPT) of a Solar Photo-Voltaic System
2012 IEEE India International Conference on Power Electronics (IICPE)
,
Delhi, India
,
Dec. 6–8
.
8.
Sweidan
,
T. O.
, and
Widyan
,
M. S.
,
2017
, “
Perturbation and Observation as MPPT Algorithm Applied on the Transient Analysis of PV-Powered DC Series Motor
,”
8th International Renewable Energy Congress (IREC)
,
Amman, Jordan
,
Mar. 21–23
, pp.
1
6
.
9.
Kamran
,
M.
,
Mudassar
,
M.
,
Fazal
,
M. R.
,
Asghar
,
M. U.
,
Bilal
,
M.
, and
Asghar
,
R.
,
2018
, “
Implementation of Improved Perturb & Observe MPPT Technique With Confined Search Space for Standalone Photovoltaic System
,”
J. King Saud Univ.—Eng. Sci.
,
32
(
1
), pp.
432
441
. 10.1016/j.jksues.2018.04.006
10.
Putri
,
R. I.
,
Wibowo
,
S.
, and
Rifa’i
,
M.
,
2015
, “
Maximum Power Point Tracking for Photovoltaic Using Incremental Conductance Method
,”
Energy Procedia
,
68
, pp.
22
30
. 10.1016/j.egypro.2015.03.228
11.
Safari
,
A.
, and
Mekhilef
,
S.
,
2011
, “
Incremental Conductance MPPT Method for PV Systems
,”
24th Canadian Conference on Electrical and Computer Engineering(CCECE)
,
Niagara Falls, ON, Canada
,
May 8–11
, pp.
000345
000347
.
12.
Das
,
P.
,
2016
, “
Maximum Power Tracking Based Open Circuit Voltage Method for PV System
,”
Energy Procedia
,
90
, pp.
2
13
. 10.1016/j.egypro.2016.11.165
13.
Ch
,
S. B.
,
Kumari
,
J.
, and
Kullayappa
,
T.
,
2011
, “
Design and Analysis of Open Circuit Voltage Based Maximum Power Point Tracking for Photovoltaic System
,”
Int. J. Adv. Sci. Technol.
,
2
, pp.
51
60
.
14.
Dzung
,
P. Q.
,
Le Dinh
,
K.
,
Hong Hee
,
L.
,
Le Minh
,
P.
, and
Nguyen Truong Dan
,
V.
,
2010
, “
The New MPPT Algorithm Using ANN-Based PV
,”
International Forum on Strategic Technology
,
Ulsan, South Korea
,
Oct. 13–15
, pp.
402
407
.
15.
Mahamudul
,
H.
,
Saad
,
M.
, and
Ibrahim Henk
,
M.
,
2013
, “
Photovoltaic System Modeling With Fuzzy Logic Based Maximum Power Point Tracking Algorithm
,”
Int. J. Photoenergy
,
2013
, p.
762946
. 10.1155/2013/762946
16.
Liu
,
Y.
,
Huang
,
S.
,
Huang
,
J.
, and
Liang
,
W.
,
2012
, “
A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions
,”
IEEE Trans. Energy Convers.
,
27
(
4
), pp.
1027
1035
. 10.1109/TEC.2012.2219533
17.
Aldair
,
A. A.
,
Obed
,
A. A.
, and
Halihal
,
A. F.
,
2018
, “
Design and Implementation of ANFIS-Reference Model Controller Based MPPT Using FPGA for Photovoltaic System
,”
Renew. Sustain. Energy Rev.
,
82
, pp.
2202
2217
. 10.1016/j.rser.2017.08.071
18.
D'Souza
,
N. S.
,
Lopes
,
L. A.
, and
Liu
,
X.
,
2010
, “
Comparative Study of Variable Size Perturbation and Observation Maximum Power Point Trackers for PV Systems
,”
Electric Power Syst. Res.
,
80
(
3
), pp.
296
305
. 10.1016/j.epsr.2009.09.012
19.
Kamala Devi
,
V.
,
Premkumar
,
K.
,
Bisharathu Beevi
,
A.
, and
Ramaiyer
,
S.
,
2017
, “
A Modified Perturb & Observe MPPT Technique to Tackle Steady State and Rapidly Varying Atmospheric Conditions
,”
Sol. Energy
,
157
, pp.
419
426
. 10.1016/j.solener.2017.08.059
20.
Kumar
,
A.
,
Chaudhary
,
P.
, and
Rizwan
,
M.
,
2015
, “
Development of Fuzzy Logic Based MPPT Controller for PV System at Varying Meteorological Parameters
,”
2015 Annual IEEE India Conference (INDICON)
,
New Delhi, India
,
Dec. 17–20
, pp.
1
6
.
21.
Lyden
,
S.
, and
Haque
,
M. E.
,
2015
, “
Maximum Power Point Tracking Techniques for Photovoltaic Systems: A Comprehensive Review and Comparative Analysis
,”
Renew. Sustain. Energy Rev.
,
52
, pp.
1504
1518
. 10.1016/j.rser.2015.07.172
22.
Ben Salah
,
C.
, and
Ouali
,
M.
,
2011
, “
Comparison of Fuzzy Logic and Neural Network in Maximum Power Point Tracker for PV Systems
,”
Electric Power Syst. Res.
,
81
(
1
), pp.
43
50
. 10.1016/j.epsr.2010.07.005
23.
Gupta
,
A.
,
Kumar
,
P.
,
Pachauri
,
R. K.
, and
Chauhan
,
Y. K.
,
2014
, “
Performance Analysis of Neural Network and Fuzzy Logic Based MPPT Techniques for Solar PV Systems
,”
2014 6th IEEE Power India International Conference (PIICON)
,
Delhi, India
,
Dec. 5–7
, IEEE, pp.
1
6
.
24.
Chim
,
C. S.
,
Neelakantan
,
P.
,
Yoong
,
H. P.
, and
Teo
,
K. T. K.
,
2011
, “
Fuzzy Logic Based MPPT for Photovoltaic Modules Influenced by Solar Irradiation and Cell Temperature
,”
2011 UkSim 13th International Conference on Computer Modelling and Simulation
,
Cambridge, UK
,
Mar. 30–Apr. 1
, IEEE, pp.
376
381
.
25.
Menniti
,
D.
,
Pinnarelli
,
A.
, and
Brusco
,
G.
,
2011
, “
Implementation of a Novel Fuzzy-Logic Based MPPT for Grid-Connected Photovoltaic Generation System
,”
2011 IEEE Trondheim PowerTech
,
Trondheim, Norway
,
June 19–23
, IEEE, pp.
1
7
.
26.
Jiang
,
L. L.
,
Nayanasiri
,
D. R.
,
Maskell
,
D. L.
, and
Vilathgamuwa
,
D. M.
,
2015
, “
A Hybrid Maximum Power Point Tracking for Partially Shaded Photovoltaic Systems in the Tropics
,”
Renew. Energy
,
76
, pp.
53
65
. 10.1016/j.renene.2014.11.005
27.
Khaehintung
,
N.
,
Sirisuk
,
P.
, and
Kurutach
,
W.
,
2003
, “
A Novel ANFIS Controller for Maximum Power Point Tracking in Photovoltaic Systems
,”
The Fifth International Conference on Power Electronics and Drive Systems (PEDS)
,
Singapore
,
Nov. 17–20
, pp.
833
836
, Vol.
2
.
28.
Khosrojerdi
,
F.
,
Taheri
,
S.
, and
Cretu
,
A.
,
2016
, “
An Adaptive Neuro-Fuzzy Inference System-Based MPPT Controller for Photovoltaic Arrays
,”
2016 IEEE Electrical Power and Energy Conference (EPEC)
,
Ottawa, ON, Canada
,
Oct. 12–14
, pp.
1
6
.
29.
Noman
,
A. M.
,
Addoweesh
,
K. E.
, and
Alolah
,
A. I.
,
2017
, “
Simulation and Practical Implementation of ANFIS-Based MPPT Method for PV Applications Using Isolated Ćuk Converter
,”
Int. J. Photoenergy
,
2017
, p.
3106734
. 10.1155/2017/3106734
30.
Sarhan
,
M. A.
,
Ding
,
M.
,
Chen
,
X.
, and
Wu
,
M.
,
2017
, “
Performance Study of Neural Network and ANFIS Based MPPT Methods For Grid Connected PV System
,”
Proceedings of the 2017 VI International Conference on Network
,
Communication and Computing, Kunming, China
,
December
.
31.
Duwadi
,
K.
,
2015
,
Design of ANN based MPPT for Solar Panel.
.
32.
Worku
,
M.
, and
Abido
,
M.
,
2016
, “
Grid Connected PV System Using ANFIS Based MPPT Controller in Real Time
,”
International Conference on Renewable Energies and Power Quality (ICREPQ 16)
,
Madrid, Spain
,
May 4–6
.
33.
Al-Majidi
,
S. D.
,
Abbod
,
M. F.
, and
Al-Raweshidy
,
H. S.
,
2019
, “
Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data
,”
Electronics
,
8
(
8
), p.
858
. 10.3390/electronics8080858
34.
Hussein
,
H.
,
Aloui
,
A.
, and
AlShammari
,
B.
,
2018
, “
ANFIS-Based PI Controller for Maximum Power Point Tracking in PV Systems
,”
Int. J. Adv. Appl. Sci.
,
5
(
2
), pp.
90
96
. 10.21833/ijaas.2018.02.015
35.
Abido
,
M.
,
Khalid
,
M. S.
, and
Worku
,
M. Y.
,
2015
, “
An Efficient ANFIS-Based PI Controller for Maximum Power Point Tracking of PV Systems
,”
Arabian J. Sci. Eng.
,
40
(
9
), pp.
2641
2651
. 10.1007/s13369-015-1749-z
36.
Al-Hmouz
,
A.
,
Shen
,
J.
,
Al-Hmouz
,
R.
, and
Yan
,
J.
,
2011
, “
Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
,”
IEEE Trans. Learn. Technol.
,
5
(
3
), pp.
226
237
. 10.1109/TLT.2011.36
37.
Hakim
,
S. J. S.
,
Razak
,
H. A.
,
Allemang
,
R.
,
De Clerck
,
J.
,
Nieziecki
,
C.
, and
Blough
,
J. R
,
2012
,
Topics in Modal Analysis I
, 5th ed., Vol.
5
,
Springer
,
New York
, pp.
399
405
.
38.
Bhatia
,
S.
,
2014
,
Advanced Renewable Energy Systems (Part 1 and 2)
,
CRC Press
,
New York
.
39.
Bücher
,
K.
,
1997
, “
Site Dependence of the Energy Collection of PV Modules
,”
Sol. Energy Mater. Sol. Cells
,
47
(
1–4
), pp.
85
94
. 10.1016/S0927-0248(97)00028-7
40.
Kollimalla
,
S. K.
, and
Mishra
,
M. K.
,
2014
, “
A Novel Adaptive P&O MPPT Algorithm Considering Sudden Changes in the Irradiance
,”
IEEE Trans. Energy Convers.
,
29
(
3
), pp.
602
610
. 10.1109/TEC.2014.2320930
41.
Nedumgatt
,
J. J.
,
Jayakrishnan
,
K. B.
,
Umashankar
,
S.
,
Vijayakumar
,
D.
, and
Kothari
,
D. P.
,
2011
, “
Perturb and Observe MPPT Algorithm for Solar PV Systems-Modeling and Simulation
,”
2011 Annual IEEE India Conference
,
Hyderabad, India
,
Dec. 16–18
, pp.
1
6
.
42.
Elgendy
,
M. A.
,
Zahawi
,
B.
, and
Atkinson
,
D. J.
,
2011
, “
Assessment of Perturb and Observe MPPT Algorithm Implementation Techniques for PV Pumping Applications
,”
IEEE Trans. Sustain. Energy
,
3
(
1
), pp.
21
33
. 10.1109/TSTE.2011.2168245
43.
Elgendy
,
M.
,
Zahawi
,
B.
, and
Atkinson
,
D.
,
2012
, “
Evaluation of Perturb and Observe MPPT Algorithm Implementation Techniques
,”
6th IET International Conference on Power Electronics, Machines and Drives (PEMD 2012)
,
Bristol, UK
,
Mar. 27–29
.
44.
Abdelsalam
,
A. K.
,
Massoud
,
A. M.
,
Ahmed
,
S.
, and
Enjeti
,
P. N.
,
2011
, “
High-Performance Adaptive Perturb and Observe MPPT Technique for Photovoltaic-Based Microgrids
,”
IEEE Trans. Power Electron.
,
26
(
4
), pp.
1010
1021
.
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