A new advanced third-order sliding mode control with adaptive gain adjustment using fuzzy logic technique for standalone photovoltaic systems

Nesrine Cherigui; Abdelkarim Chemidi; Ahmed Tahour; Mohamed Horch; Nesrine Cherigui; Abdelkarim Chemidi; Ahmed Tahour; Mohamed Horch

doi:10.3934/electreng.2025012

AIMS Electronics and Electrical Engineering

2025, Volume 9, Issue 2: 243-259. doi: 10.3934/electreng.2025012

Previous Article Next Article

Research article

A new advanced third-order sliding mode control with adaptive gain adjustment using fuzzy logic technique for standalone photovoltaic systems

1.
Ecole supérieure en sciences Appliquées ESSA, Tlemcen 13000, Algeria
2.
Laboratoire d'automatique de Tlemcen
3.
Manufacturing Engineering Laboratory Of Tlemcen
4.
School of Electrical and Energetic Engineering of Oran, 31000, Algeria

Academic Editor: Psomopoulos Konstantinos

Received: 17 February 2025 Revised: 16 April 2025 Accepted: 18 April 2025 Published: 25 April 2025

The nonlinear behavior of photovoltaic (PV) panels, coupled with their strong dependence on atmospheric conditions, necessitates efficient maximum power point tracking (MPPT) techniques to maximize power extraction, reduce costs, and enhance overall system efficiency. The output power of a PV panel is significantly influenced by current-voltage (I-V) and power-voltage (P-V) characteristics, as well as variations in solar irradiance and temperature. Consequently, advanced MPPT control strategies are required to ensure fast transient response, robustness against disturbances, and minimal steady-state oscillations. This paper proposed a novel MPPT strategy based on a fuzzy third-order sliding mode controller (FTOSMC) designed for PV systems. The proposed approach integrated fuzzy logic control (FLC) with third-order sliding mode control (TOSMC) to adaptively adjust control gains in real time, thereby minimizing chattering, improving tracking speed, and disturbances rejection. The effectiveness of the proposed FTOSMC was evaluated through extensive MATLAB/Simulink simulations, comparing its performance against SMC and TOSMC. Simulation results demonstrated that the proposed FTOSMC-based MPPT method outperformed conventional techniques in terms of faster convergence to the maximum power point (MPP), reduced chattering effects, and improved robustness against environmental fluctuations. These advantages made it a promising solution for PV applications, where system stability and energy efficiency were critical concerns.
- MPPT,
- fuzzy logic controller,
- third order sliding mode,
- photovoltaic system
Citation: Nesrine Cherigui, Abdelkarim Chemidi, Ahmed Tahour, Mohamed Horch. A new advanced third-order sliding mode control with adaptive gain adjustment using fuzzy logic technique for standalone photovoltaic systems[J]. AIMS Electronics and Electrical Engineering, 2025, 9(2): 243-259. doi: 10.3934/electreng.2025012

Related Papers:

Abstract

The nonlinear behavior of photovoltaic (PV) panels, coupled with their strong dependence on atmospheric conditions, necessitates efficient maximum power point tracking (MPPT) techniques to maximize power extraction, reduce costs, and enhance overall system efficiency. The output power of a PV panel is significantly influenced by current-voltage (I-V) and power-voltage (P-V) characteristics, as well as variations in solar irradiance and temperature. Consequently, advanced MPPT control strategies are required to ensure fast transient response, robustness against disturbances, and minimal steady-state oscillations. This paper proposed a novel MPPT strategy based on a fuzzy third-order sliding mode controller (FTOSMC) designed for PV systems. The proposed approach integrated fuzzy logic control (FLC) with third-order sliding mode control (TOSMC) to adaptively adjust control gains in real time, thereby minimizing chattering, improving tracking speed, and disturbances rejection. The effectiveness of the proposed FTOSMC was evaluated through extensive MATLAB/Simulink simulations, comparing its performance against SMC and TOSMC. Simulation results demonstrated that the proposed FTOSMC-based MPPT method outperformed conventional techniques in terms of faster convergence to the maximum power point (MPP), reduced chattering effects, and improved robustness against environmental fluctuations. These advantages made it a promising solution for PV applications, where system stability and energy efficiency were critical concerns.

References

[1]	Althobaiti A, Ullah N, Belkhier Y, Jamal Babqi A, Alkhammash HI, Ibeas A (2023) Expert knowledge based proportional resonant controller for three phase inverter under abnormal grid conditions. International Journal of Green Energy 20: 767–783. https://doi.org/10.1080/15435075.2022.2107395 doi: 10.1080/15435075.2022.2107395
[2]	Ullah N, Sami I, Jamal Babqi A, Alkhammash HI, Belkhier Y, Althobaiti A, et al. (2023) Processor in the loop verification of fault tolerant control for a three phase inverter in grid connected pv system. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 45: 3760–3776. https://doi.org/10.1080/15567036.2021.2015486 doi: 10.1080/15567036.2021.2015486
[3]	Katche ML, Makokha AB, Zachary SO, Adaramola MS (2023) A comprehensive review of maximum power point tracking (mppt) techniques used in solar pv systems. Energies 16: 2206. https://doi.org/10.3390/en16052206 doi: 10.3390/en16052206
[4]	Başoğlu ME (2022) Comprehensive review on distributed maximum power point tracking: Submodule level and module level mppt strategies. Solar Energy 241: 85–108. https://doi.org/10.1016/j.solener.2022.05.039 doi: 10.1016/j.solener.2022.05.039
[5]	Ali Z, Abbas SZ, Mahmood A, Ali SW, Javed SB, Su CL (2023) A study of a generalized photovoltaic system with mppt using perturb and observer algorithms under varying conditions. Energies 16: 3638. https://doi.org/10.3390/en16093638 doi: 10.3390/en16093638
[6]	Sadick A (2023) Maximum power point tracking simulation for photovoltaic systems using perturb and observe algorithm. https://doi.org/10.5772/intechopen.111632
[7]	John R, Mohammed SS, Zachariah R (2017) Variable step size perturb and observe mppt algorithm for standalone solar photovoltaic system. In 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), 1–6. IEEE. https://doi.org/10.1109/ITCOSP.2017.8303163
[8]	Bakar Siddique MA, Asad A, Asif RM, Rehman AU, Sadiq MT, Ullah I (2023) Implementation of incremental conductance mppt algorithm with integral regulator by using boost converter in grid-connected pv array. IETE Journal of Research 69: 3822–3835. https://doi.org/10.1080/03772063.2021.1920481 doi: 10.1080/03772063.2021.1920481
[9]	Yılmaz M, Corapsiz M (2022) Artificial neural network based mppt algorithm with boost converter topology for stand-alone pv system. Erzincan University Journal of Science and Technology 15: 242–257. https://doi.org/10.18185/erzifbed.1002823 doi: 10.18185/erzifbed.1002823
[10]	Ullah K, Ishaq M, Tchier F, Ahmad H, Ahmad Z (2023) Fuzzy-based maximum power point tracking (mppt) control system for photovoltaic power generation system. Results in Engineering 20: 101466. https://doi.org/10.1016/j.rineng.2023.101466 doi: 10.1016/j.rineng.2023.101466
[11]	Babaie M, Sharifzadeh M, Mehrasa M, Chouinard G, Al-Haddad K (2020) Pv panels maximum power point tracking based on ann in three-phase packed e-cell inverter. In 2020 IEEE International Conference on Industrial Technology (ICIT), 854–859. IEEE. https://doi.org/10.1109/ICIT45562.2020.9067218
[12]	Abouzeid AF, Eleraky H, Kalas A, Rizk R, Elsakka MM, Refaat A (2024) Experimental validation of a low-cost maximum power point tracking technique based on artificial neural network for photovoltaic systems. Scientific Reports 14: 18280. https://doi.org/10.1038/s41598-024-67306-0 doi: 10.1038/s41598-024-67306-0
[13]	Chou KY, Yeh YW, Chen YT, Cheng YM, Chen YP (2020) Adaptive neuro fuzzy inference system based mppt algorithm applied to photovoltaic systems under partial shading conditions. In 2020 International Automatic Control Conference (CACS), 1–6. IEEE. https://doi.org/10.1109/CACS50047.2020.9289733
[14]	Vo TH (2023) Design of the adaptive neuro-fuzzy inference system (anfis) and a genetic algorithm controller for solar photovoltaic systems using the boost converter. Journal of Technical Education Science 18: 109–118. https://doi.org/10.54644/jte.78A.2023.1439 doi: 10.54644/jte.78A.2023.1439
[15]	Dkhichi F (2023) Improved mppt algorithm: Artificial neural network trained by an enhanced gauss-newton method. AIMS Electronics & Electrical Engineering 7: 380–405. https://doi.org/10.3934/electreng.2023020 doi: 10.3934/electreng.2023020
[16]	Obukhov S, Ibrahim A, Diab AAZ, Al-Sumaiti AS, Aboelsaud R (2020) Optimal performance of dynamic particle swarm optimization based maximum power trackers for stand-alone pv system under partial shading conditions. IEEE Access 8: 20 770–20 785. https://doi.org/10.1109/ACCESS.2020.2966430 doi: 10.1109/ACCESS.2020.2966430
[17]	Dagal I, Akın B, Akboy E (2022) Mppt mechanism based on novel hybrid particle swarm optimization and salp swarm optimization algorithm for battery charging through simulink. Scientific reports 12: 2664. https://doi.org/10.1038/s41598-022-06609-6 doi: 10.1038/s41598-022-06609-6
[18]	Belmadani H, Bradai R, Kheldoun A, Mohammed KK, Mekhilef S, Belkhier Y, et al. (2024) A new fast and efficient mppt algorithm for partially shaded pv systems using a hyperbolic slime mould algorithm. International Journal of Energy Research 2024: 5585826. https://doi.org/10.1155/2024/5585826 doi: 10.1155/2024/5585826
[19]	Messaoudi F, Farhani F, Zaafouri A (2024) A new approach to mppt hybrid incremental conductance-sliding mode control for pv grid-connected. Measurement and Control 57:1370–1382. https://doi.org/10.1177/00202940241240666 doi: 10.1177/00202940241240666
[20]	Benbouhenni H, Bizon N (2021) Third-order sliding mode applied to the direct field-oriented control of the asynchronous generator for variable-speed contra-rotating wind turbine generation systems. Energies 14: 5877. https://doi.org/10.3390/en14185877 doi: 10.3390/en14185877
[21]	Bouhadji F, Bouyakoub I, Mehedi F, Kacemi WM, Reguieg Z (2024) Optimization of grid power quality using third order sliding mode controller in pv systems with multilevel inverter. Energy Reports 12: 5177–5193. https://doi.org/10.1016/j.egyr.2024.10.064 doi: 10.1016/j.egyr.2024.10.064
[22]	Hassan Q, Viktor P, Al-Musawi TJ, Ali BM, Algburi S, Alzoubi HM, et al. (2024) The renewable energy role in the global energy transformations. Renewable Energy Focus 48: 100545. https://doi.org/10.1016/j.ref.2024.100545 doi: 10.1016/j.ref.2024.100545
[23]	Manuel NL, İnanç N (2022) Sliding mode control-based mppt and output voltage regulation of a stand-alone pv system. Power Electronics and Drives 7: 159–173. https://doi.org/10.2478/pead-2022-0012 doi: 10.2478/pead-2022-0012
[24]	Heydari M, Heydari A, Amini M (2023) Solar power generation and sustainable energy: a review. International Journal of Technology and Scientific Research 12: 342–349.
[25]	Zhang Y, Wang YJ, Yu JQ (2022) A novel mppt algorithm for photovoltaic systems based on improved sliding mode control. Electronics 11: 2421. https://doi.org/10.3390/electronics11152421 doi: 10.3390/electronics11152421
[26]	Jamshidi F, Salehizadeh MR, Yazdani R, Azzopardi B, Jately V (2023) An improved sliding mode controller for mpp tracking of photovoltaics. Energies 16: 2473. https://doi.org/10.3390/en16052473 doi: 10.3390/en16052473

Reader Comments

Your name:*

Email:*
© 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)