Simulation and
Experimental Assessment of Various MPPT Methods for Photovoltaic Systems
Maximum
Power Point Tracking (MPPT) is a power electronic system used to control the
electrical circuit that interfaces with the photovoltaic array and adjusts the
electrical operating point of the module to operate at its MPP. Several methods
have been developed over the years to implement MPPT effectively. This paper
covers the simulation and experimental implementation of several MPPT methods.
Among these methods are conventional, hybrid, and soft competing methods. The
simulations were carried out using MATLAB/SIMULINK. The Arduino Mega 2560
controller implemented the hardware configuration and real-time data
acquisition. All experimental work is done at the Renewable Energies Laboratory
of the University of Jijel. A thorough explanation of the platforms for
implementing software and hardware is given. Test the flexibility and
comparison of the MPPT technique has been performed. The results of MPPT
methods obtained from simulation and experimental setups were presented and
discussed.
[1]
International Energy Agency [Online]. Available.at http://www.iea.org/. [Accessed: Jan. 03, 2026].
[2] Abdulaziz F. Shendi, Ahmad M. Nahhas. “Power generation by using
Photovoltaic systems for Yanbu and Rabigh regions in Saudi Arabia: a
costeffective study”. Islamic University Journal of Applied Sciences, Volume V,
Issue I, July 2023, Pages 10-44. https://doi.org/10.63070/jesc.2023.001.
[3]
Habib Muhammad Usman1,
Nirma Kumari Sharma, Sani Saminu, Abdulbasit Bashir Yero, Abdurrahman Salisu
Yahya, Farouk Isah Muhammad. “Enhancing Photovoltaic Efficiency through Engine
Oil Coatings: A Comparative Analysis of New, Partially Used, and Degraded Oils”.
The Islamic University Journal of Applied Sciences, Issue II, Volume VI,
December 2024, Pages 134-154. . https://doi.org/10.63070/jesc.2024.018.
[4] Motahhir S, El Ghzizal A, Sebti S, Derouich A. MIL and SIL and PIL tests
for MPPT algorithm. Cogent Engineering. 2017 Jan 1 ;4(1) :1378475.
[5] Rouibah, N., Elhammoumi, A., Bouttout, A., Haddad, S., Oukaci, S., Limam,
A. and Benghanem, M., 2025. Smart monitoring of photovoltaic energy systems: An
IoT-based prototype approach. Scientific African, p.e02973.
[6] Messai A, Mellit A. Design and
implementation of maximum power point tracking algorithm using fuzzy logic and
genetic algorithm. In Assessment and Simulation Tools for Sustainable Energy
Systems 2013 (pp. 285-307). Springer, London.
[7] Zainuri MA, Radzi MA, Che Soh A, Rahim NA. Development of
adaptive perturb and observe?fuzzy control maximum power point tracking for
photovoltaic boost dc–dc converter. IET Renewable Power Generation. 2014
Mar;8(2):183-94.
[8]
Rouibah, N., Barazane, L., Rabhi, A., Hajji, B.,
Bouhedir, R., Hamied, A. and Mellit, A., 2021. Experimental Assessment of
Perturb & Observe, Incremental Conductance and Hill Climbing MPPTs for
Photovoltaic Systems. In Proceedings of the 2nd International Conference on
Electronic Engineering and Renewable Energy Systems: ICEERE 2020, 13-15 April
2020, Saidia, Morocco (pp. 461-467). Springer Singapore.
[9] Li, P., Zhang, J., Xu, R., Zhou, J. and Gao, Z., 2024. Integration of MPPT
algorithms with spacecraft applications: Review, classification and future
development outlook. Energy, 308, p.132927.
[10]
F. Belhachat,
C.Larbes,A review of global maximum power point tracking techniques of pv
system under partial shading conditions, Renewable and Sustainable Energy
Reviews (2018) 513–553.
[11]
Villegas-Mier, C.G., Rodriguez-Resendiz, J.,
?lvarez-Alvarado, J.M., Rodriguez-Resendiz, H., Herrera-Navarro, A.M. and
Rodr?guez-Abreo, O., 2021. Artificial neural networks in MPPT algorithms for
optimization of photovoltaic power systems: A review. Micromachines, 12(10),
p.1260.
[12]
D Rekioua, E.
Matagne, Optimization of photovoltaic power systems: modelization, simulation
and control, Springer Science & Business Media (2012) 113–148.
[13]
Rouibah, N.,
Bouttout, A., Oukaci, S., Limam, A. and Derradji, L., 2024, October. The Recent
Global MPPT Methods: State-of-the-Art. In International Conference on
Artificial Intelligence in Renewable Energetic Systems (pp. 504-515).
Cham: Springer Nature Switzerland.
[14]
B Bendib, H
Belmili, F Krim, A survey of the most used MPPT methods: Conventional and
advanced algorithms applied for photovoltaic systems, Renewable and Sustainable
Energy Reviews 45 (2015) 637–648.
[15]
Z Salam, J Ahmed,
BS Merugu, the application of soft computing methods for MPPT of PV system: A
technological and status review, Applied energy 107 (2013)135–148.
[16]
Ali, A.I.M., Mousa, H.H., Mohamed, H.R.A., Kamel,
S., Hassan, A.S., Alaas, Z.M., Mohamed, E.E. and Abdallah, A.R.Y., 2023. An
enhanced P&O MPPT algorithm with concise search area for grid-tied PV
systems. IEEE Access, 11, pp.79408-79421.
[17]
Jately, V., Azzopardi, B., Joshi, J., Sharma, A.
and Arora, S., 2021. Experimental Analysis of hill-climbing MPPT algorithms
under low irradiance levels. Renewable and Sustainable Energy Reviews, 150,
p.111467.
[18]
HA Sher, AF
Murtaza, A Noman, KE Addoweesh, K Al-Haddad, M Chiaberge, A new sensorless
hybrid MPPT algorithm based on fractional short-circuit current measurement and
P&O MPPT, IEEE Transactions on Sustainable Energy 6 (2015).
[19]
De Brito, M. A. G.,
Galotto, L., Sampaio, L. P., e Melo, G. d. A., et Canesin, C. A., “Evaluation
of the main MPPT techniques for photovoltaic applications,” IEEE transactions
on industrial electronics, V. 60, no 3, (2013), 1156-1167.
[20]
Zemmit, A., Loukriz, A., Belhouchet, K., Alharthi,
Y.Z., Alshareef, M., Paramasivam, P. and Ghoneim, S.S., 2025. GWO and WOA
variable step MPPT algorithms-based PV system output power optimization. Scientific
Reports, 15(1), p.7810.
[21]
Bouchakour, A., Zarour, L., Bessous, N., Bechouat,
M., Borni, A., Zaghba, L., Rabehi, A., Alwabli, A., El-Abd, M. and Ghoneim,
S.S., 2024. MPPT algorithm based on metaheuristic techniques (PSO & GA)
dedicated to improve wind energy water pumping system performance. scientific
reports, 14(1), p.17891.
[22]
Celikel, R., Aydogmus, O. and Yilmaz, M., 2025. A
modified perturb and observe MPPT algorithm for PEMFC with rapid convergence
and low power oscillation. Scientific Reports, 15(1),
p.27848.
[23]
Xia, K., Li, Y. and Zhu, B., 2024. Improved
photovoltaic MPPT algorithm based on ant colony optimization and fuzzy logic
under conditions of partial shading. IEEE Access, 12,
pp.44817-44825.
[24]
Bouksaim, M., Mekhfioui, M. and Srifi, M.N., 2025,
September. A Comprehensive Decade-Long Review of Advanced MPPT Algorithms for
Enhanced Photovoltaic Efficiency. In Solar (Vol. 5, No. 3, p. 44).
MDPI.