Photovoltaic (PV) systems are increasingly significant contributors to global energy supply, currently accounting for 4% of total needs. However, extracting maximum power from these systems presents a complex challenge due to the nonlinear nature of their electrical characteristics. Factors like temperature fluctuations and varying irradiance levels can significantly impact power output. This work contributes a novel design, testing, and validation methodology for robust Maximum Power Point Tracking (MPPT) control in PV systems. The methodology leverages two newly developed artificial intelligence (AI)-based nonlinear control strategies, Adaptive Neuro-Fuzzy Inference System based on Terminal Sliding Mode Control (ANFIS-TSMC), and Backstepping combined with ANFIS (ANFIS-BS). A validated Model-Based Design (MBD) approach and a two-stage testing sequence provide a comprehensive framework for developing and verifying embedded software for MPPT algorithms. Evaluation results demonstrate the proposed system’s ability to control reference power precisely under diverse atmospheric conditions. Successful integration with a 32-bit ARM microcontroller also paves the way for real-world implementation. The embedded software consistently exhibited high compliance and performance in meeting MPPT requirements across various test scenarios.
AliKKhanLKhanQ, et al. (2019) Robust integral backstepping based nonlinear MPPT control for a pv system. Energies12(16): 3180.
2.
AnakwaWKCohenENaikA, et al. (2001) Tools for rapid prototyping of embedded control systems. In: IECON’01. 27th Annual conference of the IEEE industrial electronics society (Cat. No. 37243), vol. 1, Denver, CO, 6 August, pp. 90–94. New York: IEEE.
3.
ArsieIBettaGCapriglioneD, et al. (2014) Functional testing of measurement-based control systems: An application to automotive. Measurement54: 222–233.
4.
AwanMMAJavedMYAsgharAB, et al. (2022) Performance optimization of a ten check MPPT algorithm for an off-grid solar photovoltaic system. Energies15(6): 2104.
5.
AygülKCikanMDemirdelenT, et al. (2023) Butterfly optimization algorithm based maximum power point tracking of photovoltaic systems under partial shading condition. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects45(3): 8337–8355.
6.
BollipoRBMikkiliSBonthagorlaPK (2020) Critical review on PV MPPT techniques: Classical, intelligent and optimisation. IET Renewable Power Generation14(9): 1433–1452.
7.
BoopathiMSujathaRSenthil KumarC, et al. (2017) Quantification of software code coverage using artificial bee colony optimization based on Markov approach. Arabian Journal for Science and Engineering42: 3503–3519.
8.
CelikelRYilmazMGundogduA (2022) A voltage scanning-based MPPT method for PV power systems under complex partial shading conditions. Renewable Energy184: 361–373.
9.
CheddadiYErrahimiFEs-SbaiN (2018) Design and verification of photovoltaic MPPT algorithm as an automotive-based embedded software. Solar Energy171: 414–425.
10.
ChoiHCiobotaruMJangM, et al. (2015) Performance of medium-voltage DC-bus PV system architecture utilizing high-gain DC–DC converter. IEEE Transactions on Sustainable Energy6(2): 464–473.
11.
ChtitaSMotahhirSEl GhzizalA (2024) New design and embedded implementation of a low-cost maximum power point tracking charge controller for stand-alone photovoltaic systems. Energy Technology12: 2301324.
12.
De LemosRGieseHMüllerHA, et al. (2013) Software engineering for self-adaptive systems: A second research roadmap. In: Software engineering for self-adaptive systems II: International seminar, Dagstuhl Castle, Lecture Notes in Computer Science, vol. 7475, Berlin, 24–29 October. Cham: Springer.
13.
ElbasetAAAliHAbd-El SattarM, et al. (2016) Implementation of a modified perturb and observe maximum power point tracking algorithm for photovoltaic system using an embedded microcontroller. IET Renewable Power Generation10(4): 551–560.
14.
FarajiRRouholaminiANajiHR, et al. (2014) FPGA-based real time incremental conductance maximum power point tracking controller for photovoltaic systems. IET Power Electronics7(5): 1294–1304.
15.
FaresDFathiMShamsI, et al. (2021) A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions. Energy Conversion and Management230: 113773.
16.
GomezCHB (1996) Nonlinear decoupling of temperature and relative humidity for an air conditioning system. Mayaguez, Puerto Rico: University of Puerto Rico, Mayaguez.
17.
GrahamDR (1991) Software testing tools: A new classification scheme. Software Testing, Verification and Reliability1(3): 17–34.
18.
HillDde BeeckJOBajaM, et al. (2017) Use of V-cycle methodology to develop mechatronic fuel system functions. Technical report, SAE Technical Paper.
19.
HommesQVE (2012) Review and assessment of the ISO 26262 draft road vehicle-functional safety. Technical report, SAE Technical Paper.
20.
HousseinEHMahdyMAFathyA, et al. (2021) A modified Marine Predator Algorithm based on opposition based learning for tracking the global MPP of shaded PV system. Expert Systems With Applications183: 115253.
21.
JiangZLeonardRDougalR, et al. (2004) Processor-in-the-loop simulation, real-time hardware-in-the-loop testing, and hardware validation of a digitally-controlled, fuel-cell powered battery-charging station. In: 2004 IEEE 35th annual power electronics specialists conference (IEEE Cat. No. 04CH37551), vol. 3, Aachen, 20–25 June, pp. 2251–2257. New York: IEEE.
22.
KarmouniHChouiekhMMotahhirS, et al. (2022) A fast and accurate sine-cosine MPPT algorithm under partial shading with implementation using arduino board. Cleaner Engineering and Technology9: 100535.
23.
KilliMSamantaS (2015) Modified perturb and observe MPPT algorithm for drift avoidance in photovoltaic systems. IEEE transactions on Industrial Electronics62(9): 5549–5559.
24.
Krishnan GSKinattingalSSimonSP, et al. (2020) MPPT in PV systems using ant colony optimisation with dwindling population. IET Renewable Power Generation14(7): 1105–1112.
25.
KuoYCLiangTJChenJF (2001) Novel maximum-power-point-tracking controller for photovoltaic energy conversion system. IEEE Transactions on Industrial Electronics48(3): 594–601.
26.
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.
27.
LiNMingxuanMYihaoW, et al. (2019) Maximum power point tracking control based on modified ABC algorithm for shaded PV system. In: 2019 AEIT international conference of electrical and electronic technologies for automotive (AEIT AUTOMOTIVE), Turin, 2–4 July, pp. 1–5. New York: IEEE.
28.
LiuFDuanSLiuF, et al. (2008) A variable step size INC MPPT method for PV systems. IEEE Transactions on Industrial Electronics55(7): 2622–2628.
29.
Lopez-FloresDRDuran-GomezJLChacon-MurguiaMI (2020) A mechanical sensorless MPPT algorithm for a wind energy conversion system based on a modular multilayer perceptron and a processor-in-the-loop approach. Electric Power Systems Research186: 106409.
30.
Lopez-SantosOMartinez-SalameroLGarciaG, et al. (2012) Sliding-mode indirect control of the quadratic boost converter operating in continuous conduction mode or discontinuous conduction mode. In: 2012 IEEE 4th Colombian workshop on circuits and systems (CWCAS), Barranquilla, Colombia, 1–2 November, pp. 1–6. New York: IEEE.
31.
LoukrizAHaddadiMMessaltiS (2016) Simulation and experimental design of a new advanced variable step size incremental conductance MPPT algorithm for PV systems. ISA Transactions62: 30–38.
32.
LuXLinPChengS, et al. (2021) Fault diagnosis model for photovoltaic array using a dual-channels convolutional neural network with a feature selection structure. Energy Conversion and Management248: 114777.
33.
MahajanMKumarSPorwalR (2012) Applying genetic algorithm to increase the efficiency of a data flow-based test data generation approach. ACM SIGSOFT Software Engineering Notes37(5): 1–5.
34.
MathiDKChinthamallaR (2020) Global maximum power point tracking technique based on adaptive salp swarm algorithm and P&O techniques for a PV string under partially shaded conditions. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects : 1–18.
35.
MbarkiBChroutaJFarhaniF, et al. (2023) Backstepping-based artificial intelligence algorithm for photovoltaic-quadratic boost converter system. Cybernetics and Systems. Epub ahead of print 26 August. DOI: 10.1080/01969722.2023.2247265.
36.
MbarkiBFethiFChroutaJ, et al. (2024) Adaptive neuro-fuzzy inference system algorithm-based robust terminal sliding mode control MPPT for a photovoltaic system. Transactions of the Institute of Measurement and Control46(2): 316–325.
37.
MekhilefSSaidurRSafariA, et al. (2011) Biomass energy in Malaysia: Current state and prospects. Renewable and Sustainable Energy Reviews15(7): 3360–3370.
38.
MotahhirSEl GhzizalASebtiS, et al. (2017) MIL and SIL and PIL tests for MPPT algorithm. Cogent Engineering4(1): 1378475.
39.
MotahhirSEl HammoumiAEl GhzizalA (2020) The most used MPPT algorithms: Review and the suitable low-cost embedded board for each algorithm. Journal of cleaner production246: 118983.
40.
MoyYLedinotEDelsenyH, et al. (2013) Testing or formal verification: Do-178c alternatives and industrial experience. IEEE software30(3): 50–57.
41.
NkambuleMSHasanANAliA, et al. (2021) Comprehensive evaluation of machine learning MPPT algorithms for a PV system under different weather conditions. Journal of Electrical Engineering & Technology16: 411–427.
42.
RoyRBRokonuzzamanMAminN, et al. (2021) A comparative performance analysis of ANN algorithms for MPPT energy harvesting in solar PV system. IEEE Access9: 102137–102152.
43.
SaadWSellamiAGarciaG (2018) Terminal sliding mode control-based MPPT for a photovoltaic system with uncertainties. International Journal of Modelling, Identification and Control29(2): 118–126.
44.
SolarinSAAl-MulaliUOzturkI (2017) Validating the environmental Kuznets curve hypothesis in India and China: The role of hydroelectricity consumption. Renewable and Sustainable Energy Reviews80: 1578–1587.
45.
SoonTKMekhilefS (2014) A fast-converging MPPT technique for photovoltaic system under fast-varying solar irradiation and load resistance. IEEE Transactions on Industrial Informatics11(1): 176–186.
46.
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.
47.
TeyKSMekhilefSSeyedmahmoudianM, et al. (2018) Improved differential evolution-based MPPT algorithm using SEPIC for PV systems under partial shading conditions and load variation. IEEE Transactions on Industrial Informatics14(10): 4322–4333.
48.
WahlerMFerrantiESteigerR, et al. (2012) CAST: Automating software tests for embedded systems. In: 2012 IEEE fifth international conference on software testing, verification and validation, Montreal, QC, Canada, 17–21 April, pp. 457–466. New York: IEEE.
49.
WasimMSAmjadMHabibS, et al. (2022) A critical review and performance comparisons of swarm-based optimization algorithms in maximum power point tracking of photovoltaic systems under partial shading conditions. Energy Reports8: 4871–4898.
50.
YangBZhongLZhangX, et al. (2019) Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition. Journal of cleaner production215: 1203–1222.
51.
YangBZhuTWangJ, et al. (2020) Comprehensive overview of maximum power point tracking algorithms of PV systems under partial shading condition. Journal of Cleaner Production268: 121983.
52.
ZafarMHKhanNMMirzaAF, et al. (2021) A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition. Sustainable Energy Technologies and Assessments47: 101367.
53.
ZouLWangLXiaL, et al. (2017) Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems. Renewable energy106: 343–353.
54.
ZoungranaAÇakmakciM (2021) From non-renewable energy to renewable by harvesting salinity gradient power by reverse electrodialysis: A review. International Journal of Energy Research45(3): 3495–3522.
