PV SYSTEM MPPT CONTROL: A COMPARATIVE ANALYSIS OF P&O, INCCOND, SMC AND FLC ALGORITHMS

Khoukha Bouguerra; Samia Latreche; Hamza Khemlche; Mabrouk Khemliche

doi:10.35784/iapgos.6152

Open full text

PDF

Published: Dec 21, 2024

DOI: https://doi.org/10.35784/iapgos.6152

DOI

https://doi.org/10.35784/iapgos.6152

Authors

Khoukha Bouguerra

khaoukha.bouguerra@yahoo.com

Ferhat Abbas Sétif 1 University-UFAS, Electrical Engineering Department, Automatic Laboratory of Sétif-LAS

https://orcid.org/0009-0008-4522-1532

Samia Latreche

ksamia2002@yahoo.fr

Ferhat Abbas Sétif 1 University-UFAS, Electrical Engineering Department, Automatic Laboratory of Sétif-LAS

https://orcid.org/0000-0002-1496-739X

Hamza Khemlche

h.khemliche@crti.dz

Research Centre in Industrial Technologies

https://orcid.org/0000-0002-7373-780X

Mabrouk Khemliche

mabroukkhemliche@univ-setif.dz

Ferhat Abbas Sétif 1 University-UFAS, Electrical Engineering Department, Automatic Laboratory of Sétif-LAS

Abstract

The importance of solar energy is manifested in the growing demand for renewable energy sources around the world, which is fueled by environmental concern and the scarcity of conventional energy. Maximum power point trackers (MPPTS) are necessary in solar energy systems due to atmospheric changes that threaten the efficiency of solar energy systems. This article compares MPPT technologies. Including traditional and modern techniques, and despite the results achieved by classical techniques in maximizing and extracting as much energy as possible, they face a big problem in reaching the bone energy point. These provide modern technologies such as FLC and SMC. They provide exceptional accuracy and excellent response in all environmental conditions but come with additional complexity and higher cost. These technologies are ideal in applications that require high performance under continuously changing conditions or in difficult environments (such as large solar power systems or systems dealing with large fluctuations in illumination). This research aims to conduct a comprehensive study and compare of classical technologies (P&O and IncCond) and modern technologies sliding mode control (SMC, Fuzzy Logic Control – FLC), taking into account factors such as efficiency, complexity and response time. Tests were conducted under different climatic conditions to understand and enhance the efficiency of MPPT technologies. Our study highlights the enhanced performance for methods based on modern technologies. This study provides a comprehensive comparative analysis, and by improving the efficiency and reliability of solar energy systems, our research supports the advancement of sustainable energy solutions.

Keywords:

PV, DC/DC converter, MPPT techniques, fuzzy logic control, sliding mode control

References

[1] Ahmad F. F. et al.: Application of sliding mode control for maximum power point tracking of solar photovoltaic systems: A comprehensive review. Annu Rev Control 49, 2020, 173–196 [https://doi.org/10.1016/j. arcontrol.2020.04.011]. DOI: https://doi.org/10.1016/j.arcontrol.2020.04.011

[2] Bait F., Latreche S., Khemliche M.: Simulation of different faults in photovoltaic installation. 19th IEEE International Multi-Conference on Systems, Signals and Devices – SSD 2022, May 2022, 1130–1138. DOI: https://doi.org/10.1109/SSD54932.2022.9955851

[3] Belkaid A. et al.: Improving PV system performance using high efficiency fuzzy logic control. 8th International Conference on Smart Grid (icSmartGrid), 2020, 152–156. DOI: https://doi.org/10.1109/icSmartGrid49881.2020.9144817

[4] Chen J. H., Yau H. T., Hung W.: Design and Study on Sliding Mode Extremum Seeking Control of the Chaos Embedded Particle Swarm Optimization for Maximum Power Point Tracking in Wind Power Systems. Energies 7(3), 2014, 1706–1720. DOI: https://doi.org/10.3390/en7031706

[5] Derri M. et al.: Maximum Power Point Tracking using Fuzzy Logic Controller for Stand Alone photovoltaic System. International Journal of Engineering Research and Technology 3(11), 2014, 1721–1725.

[6] Djalab A., Teta A., Rezaoui M. M.: Analysis of MPPT Methods: P&O, INC and Fuzzy Logic (CLF) for a PV System. 6th International Conference on Control Engineering & Information Technology (CEIT). Turkey, Istanbul, 2018, 1–6. DOI: https://doi.org/10.1109/CEIT.2018.8751820

[7] D'Souza N. S., Lopes A. L., Liu X.: Comparative study of variable size perturbation and observation maximum power point trackers for PV systems. Electric Power Systems Research 80(3), 2010, 296–305. DOI: https://doi.org/10.1016/j.epsr.2009.09.012

[8] Fei J., Chen Y., Liu L., Fang Y.: Fuzzy multiple hidden layer recurrent neural control of nonlinear system using terminal sliding-mode controller. IEEE transactions on cybernetics 2021, 1–16.

[9] Haji D., Genc N.: Fuzzy and P&O based MPPT controllers under different conditions. 7th International Conference on Renewable Energy Research and Applications (ICRERA). 2018, 649–655. DOI: https://doi.org/10.1109/ICRERA.2018.8566943

[10] Jain K., Gupta M., Bohre A. K.: Implementation and comparative analysis of P&O and INC MPPT method for PV system. 8th IEEE India International Conference on Power Electronics (IICPE). India, Jaipur, 2018, 1–6. DOI: https://doi.org/10.1109/IICPE.2018.8709519

[11] Kish G. J., Lee J. J., Lehn P.W.: Modelling and control of photovoltaic panels utilising the incremental conductance method for maximum power point tracking. IET Renewable Power Generation 6, 2012, 259–266. DOI: https://doi.org/10.1049/iet-rpg.2011.0052

[12] Latreche S., Badoud A. E., Khemliche M.: Implementation of MPPT algorithm and supervision of shading on photovoltaic module. Engineering, Technology and Applied Science Research 8(6), 2018, 3541–3544. DOI: https://doi.org/10.48084/etasr.2354

[13] Levron Y., Shmilovitz D.: Maximum power point tracking employing sliding mode control. IEEE Transactions on Circuits and Systems I: Regular Papers 60(3), 2013, 724–732. DOI: https://doi.org/10.1109/TCSI.2012.2215760

[14] Moradi M. H. et al.: A robust hybrid method for maximum power point tracking in photovoltaic systems. Sol. Energy 94, 2013, 266–276. DOI: https://doi.org/10.1016/j.solener.2013.05.016

[15] Ram J. K. et al.: Performance enhancement of solar PV systems applying P&O assisted Flower Pollination Algorithm (FPA). Sol. Energy 199, 2020, 214–229 [https://doi.org/10.1016/j.solener.2020.02.019]. DOI: https://doi.org/10.1016/j.solener.2020.02.019

[16] Salah B. C., Ouali M.: Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems. Electric Power Systems Resarch 81, 2011, 43–50. DOI: https://doi.org/10.1016/j.epsr.2010.07.005

[17] Tey K. S., Mekhilef S.: Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level. Sol. Energy 101, 2014, 333–342. DOI: https://doi.org/10.1016/j.solener.2014.01.003

[18] Zhu Y., Fei J.: Adaptive Global Fast Terminal Sliding Mode Control of Grid connected Photovoltaic System Using Fuzzy Neural Network Approach. IEEE Access 5, 2017, 9476–9484. DOI: https://doi.org/10.1109/ACCESS.2017.2707668

Bouguerra, K., Latreche, S., Khemlche, H., & Khemliche, M. (2024). PV SYSTEM MPPT CONTROL: A COMPARATIVE ANALYSIS OF P&O, INCCOND, SMC AND FLC ALGORITHMS . Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(4), 52–62. https://doi.org/10.35784/iapgos.6152

Author Biography

Mabrouk Khemliche, Ferhat Abbas Sétif 1 University-UFAS, Electrical Engineering Department, Automatic Laboratory of Sétif-LAS

Researcher at Ferhat Abbas University of Setif

Article Sidebar

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License

Mabrouk Khemliche, Ferhat Abbas Sétif 1 University-UFAS, Electrical Engineering Department, Automatic Laboratory of Sétif-LAS