The non-linear power-current characteristics of a photovoltaic generator (GPV) present a challenge in maximizing power production. To address this issue, Maximum Power Point Tracking (MPPT) methods, such as the Adaptive Neuro-Fuzzy Inference System (ANFIS), are commonly used due to their quick response and minimal oscillation. As well, sliding mode control (SMC) is a popular method for controlling linear and nonlinear systems because of its high robustness. In this study, the principal purpose is to develop a new approach based on the combination of ANFIS and Terminal Robust Sliding Mode Control (ANFIS-TRSMC) to resist the PV system against uncertain conditions and track the optimal power point. The simulation results show that the ANFIS-TRSMC controller has an accurate, fast, and robust response in comparison with other algorithms such as perturb and observe and the maximum power voltage–based TRSMC controller.
AbdelsalamAKMassoudAMAhmedS, et al. (2011) High-performance adaptive perturb and observe MPPT technique for photovoltaic-based microgrids. IEEE Transactions on Power Electronics26(4): 1010–1021.
2.
AhmadFFGhenaiCHamidAK, et al. (2020) Application of sliding mode control for maximum power point tracking of solar photovoltaic systems: A comprehensive review. Annual Reviews in Control49: 173–196.
3.
AhmedJSalamZ (2015) An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency. Applied Energy150: 97–108.
4.
Al-DhaifallahMNassefAMRezkH, et al. (2018) Optimal parameter design of fractional order control-based INC-MPPT for PV system. Solar Energy159: 650–664.
5.
AliKKhanLKhanQ, et al. (2019) Robust integral backstepping based nonlinear MPPT control for a PV system. Energies12(16): 3180.
6.
Al-MajidiSDAbbodMFAl-RaweshidyHS (2019) Design of an intelligent MPPT based on ANN using a real photovoltaic system data. In: 2019 54th International universities power engineering conference (UPEC), Bucharest, 3–6 September, pp. 1–6. New York: IEEE.
7.
AmmarRBAmmarMBOualhaA (2020) Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems. Renewable Energy153: 1016–1028.
8.
BoumaarafHTalhaABouhaliO (2015) A three-phase NPC grid-connected inverter for photovoltaic applications using neural network MPPT. Renewable and Sustainable Energy Reviews49: 1171–1179.
9.
ChiuC-SOuyangY-LKuC-Y (2012) Terminal sliding mode control for maximum power point tracking of photovoltaic power generation systems. Solar Energy86(10): 2986–2995.
10.
ChroutaJFarhaniFZaafouriA (2021) A modified multi swarm particle swarm optimization algorithm using an adaptive factor selection strategy. Transactions of the Institute of Measurement and Control. Epub ahead of print 25July. DOI: 10.1177/01423312211029509.
11.
ChuangYLHerreraMBalalA (2022) Using PV fuzzy tracking algorithm to charge electric vehicles. International Journal of Robotics and Control Systems2(2): 253–261.
12.
DarabanSPetreusDMorelC (2014) A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading. Energy74: 374–388.
13.
DenizE (2017) ANN-based MPPT algorithm for solar PMSM drive system fed by direct-connected PV array. Neural Computing and Applications28(10): 3061–3072.
14.
DuanQMaoMDuanP, et al. (2017) An intelligent algorithm for maximum power point tracking in photovoltaic system under partial shading conditions. Transactions of the Institute of Measurement and Control39(2): 244–256.
15.
FarhatMBarambonesOSbitaL (2015) Efficiency optimization of a DSP-based standalone PV system using a stable single input fuzzy logic controller. Renewable and Sustainable Energy Reviews49: 907–920.
16.
FarhatSAlaouiRKahajiA, et al. (2013) Estimating the photovoltaic MPPT by artificial neural network. In: 2013 international renewable and sustainable energy conference (IRSEC), Ouarzazate, Morocco, 7–9 March, pp. 49–53. New York: IEEE.
17.
FemiaNPetroneGSpagnuoloG, et al. (2005) Optimization of perturb and observe maximum power point tracking method. IEEE Transactions on Power Electronics20(4): 963–973.
18.
IeaP (2018) Snapshot of global photovoltaic markets. Report IEA PVPS T-133 [Preprint].
19.
KalaiarasiNSekhar DashSParamasivamS, et al. (2021) Investigation on ANFIS aided MPPT technique for PV fed ZSI topologies in standalone applications. Journal of Applied Science and Engineering24(2): 261–269.
20.
KobayashiKMatsuoHSekineY (2004) A novel optimum operating point tracker of the solar cell power supply system. In: 2004 IEEE 35th annual power electronics specialists conference (IEEE Cat.No. 04CH37551), Aachen, 20–25 June, pp. 2147–2151. New York: IEEE.
21.
KoofigarHR (2016) Adaptive robust maximum power point tracking control for perturbed photovoltaic systems with output voltage estimation. ISA Transactions60: 285–293.
22.
KulaksızAAAkkayaR (2012) A genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor drive. Solar Energy86(9): 2366–2375.
23.
KumarYSGuptaR (2012) Maximum power point tracking of multiple photovoltaic arrays. In: 2012 students conference on engineering and systems, Allahabad, India, 16–18 March, pp. 1–6. New York: IEEE.
24.
LaneK (2018) IEA Energy Efficiency 2018 and World Energy Outlook, vol. 27, Brussels, November, pp. 1–36.
25.
LasheenMAbdel-SalamM (2018) Maximum power point tracking using Hill Climbing and ANFIS techniques for PV applications: A review and a novel hybrid approach. Energy Conversion and Management171: 1002–1019.
26.
LiuFDuanSLiuF, et al. (2008) A variable step size INC MPPT method for PV systems. IEEE Transactions on Industrial Electronics55(7): 2622–2628.
27.
MehiriABettayebMHamidA-K, et al. (2018) Fractional nonlinear synergetic control for DC-link voltage regulator of three phase inverter grid-tied PV system. In: 2018 5th international conference on renewable energy: Generation and applications (ICREGA), Al Ain, United Arab Emirates, 25–8 February, pp. 90–93. New York: IEEE.
28.
MohammadiKShamshirbandSTongCW, et al. (2015) Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of the year. Energy Conversion and Management93: 406–413.
29.
MossaMAGamOBianchiN (2022) Performance enhancement of a hybrid renewable energy system accompanied with energy storage unit using effective control system. International Journal of Robotics and Control Systems2(1): 140–171.
30.
NagarajanCUmadeviKSaravananS, et al. (2022) Performance analysis of PSO DFFP based DC-DC converter with non isolated CI using PV panel. International Journal of Robotics and Control Systems2(2): 408–423.
31.
NkambuleMSHasanANAliA, et al. (2021) Comprehensive evaluation of machine learning MPPT algorithms for a PV system under different weather conditions. Journal of Electrical Engineering & Technology16(1): 411–427.
32.
NoguchiTTogashiSNakamotoR (2002) Short-current pulse-based maximum-power-point tracking method for multiple photovoltaic-and-converter module system. IEEE Transactions on Industrial Electronics49(1): 217–223.
33.
NohH-JLeeD-YHyunD-S (2002) An improved MPPT converter with current compensation method for small scaled PV-applications. In: IEEE 2002 28th annual conference of the industrial electronics society. IECON 02, Seville, 5–8 November, pp. 1113–1118. New York: IEEE.
34.
PaulS (2013) Comparison of MPPT using GA-optimized ANN employing PI controller with GA-Optimized ANN employing fuzzy controller for PV system. In: IET Chennai fourth international conference on sustainable energy and intelligent systems (SEISCON 2013), Chennai, India, 12–14 December.
35.
RaiAKKaushikaNDSinghB, et al. (2011) Simulation model of ANN based maximum power point tracking controller for solar PV system. Solar Energy Materials and Solar Cells95(2): 773–778.
36.
RajavelARathina PrabhaN (2021) Fuzzy logic controller-based boost and buck-boost converter for maximum power point tracking in solar system. Transactions of the Institute of Measurement and Control43(4): 945–957.
37.
ReisiARMoradiMHJamasbS (2013) Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review. Renewable and Sustainable Energy Reviews19: 433–443.
38.
RezkHEltamalyAM (2015) A comprehensive comparison of different MPPT techniques for photovoltaic systems. Solar Energy112: 1–11.
39.
RezkHAlyMAl-DhaifallahM, et al. (2019) Design and hardware implementation of new adaptive fuzzy logic-based MPPT control method for photovoltaic applications. IEEE Access7: 106427–106438.
40.
RezvaniAGandomkarM (2016) Modeling and control of grid connected intelligent hybrid photovoltaic system using new hybrid fuzzy-neural method. Solar Energy127: 1–18.
41.
RizzoSAScelbaG (2015) ANN based MPPT method for rapidly variable shading conditions. Applied Energy145: 124–132.
42.
RoySKHussainSBazazMA (2017) Implementation of MPPT technique for solar PV system using ANN. In: 2017 recent developments in control, automation & power engineering (RDCAPE), Noida, India, 26–27 October, pp. 338–342. New York: IEEE.
43.
SaadNHEl-SattarAAMansourAE-AM (2016) Improved particle swarm optimization for photovoltaic system connected to the grid with low voltage ride through capability. Renewable Energy85: 181–194.
44.
SaadWSellamiAGarciaG (2018) Terminal sliding mode control-based MPPT for a photovoltaic system with uncertainties. International Journal of Modelling, Identification and Control29(2): 118–126.
45.
ShawP (2019) Modelling and analysis of an analogue MPPT-based PV battery charging system utilising DC–DC boost converter. IET Renewable Power Generation13(11): 1958–1967.
46.
SitharthanRParthasarathyTSheeba RaniS, et al. (2019) An improved radial basis function neural network control strategy-based maximum power point tracking controller for wind power generation system. Transactions of the Institute of Measurement and Control41(11): 3158–3170.
47.
SivakumarPAbdul KaderAKaliavaradhanY, et al. (2015) Analysis and enhancement of PV efficiency with incremental conductance MPPT technique under non-linear loading conditions. Renewable Energy81: 543–550.
48.
TaoHGhahremaniMAhmedFW, et al. (2021) A novel MPPT controller in PV systems with hybrid whale optimization-PS algorithm based ANFIS under different conditions. Control Engineering Practice112: 104809.
49.
TeyKSMekhilefS (2014) Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level. Solar Energy101: 333–342.
50.
WuDNarimanGSMohammedSQ, et al. (2020) Modeling and simulation of novel dynamic control strategy for PV–wind hybrid power system using FGS- PID and RBFNSM methods. Soft Computing24(11): 8403–8425.
51.
ZhangBLiXWangS (2015) A novel case adaptation method based on an improved integrated genetic algorithm for power grid wind disaster emergencies. Expert Systems with Applications42(21): 7812–7824.
52.
ZhihongMPaplinskiAPWuHR (1994) A robust MIMO terminal sliding mode control scheme for rigid robotic manipulators. IEEE Transactions on Automatic Control39(12): 2464–2469.
53.
ZhuangKYSuHYZhangKQ, et al. (2003) Adaptive terminal sliding mode control for high-order nonlinear dynamic systems. Journal of Zhejiang University. Science4(1): 58–63.
54.
ZouLWangLXiaL, et al. (2017) Prediction and comparison of solar radiation using improved empirical models and adaptive neuro-fuzzy inference systems. Renewable Energy106: 343–353.
