Comparative Analysis of Perturb and Observe and Artificial Neural Network Maximum Power Point Tracking for a 250-kW Grid-Connected Photovoltaic System

References

[1] International Energy Agency Photovoltaic Power Systems Programme. Trends in Photovoltaic Applications 2025. Report IEA PVPS T1-48:2025; 2025.

[2] Boubaker O. MPPT techniques for photovoltaic systems: a systematic review in current trends and recent advances in artificial intelligence. Discover Energy. 2023;3:9. https://doi.org/10.1007/s43937-023-00024-2

[3] Katche ML, Makokha AB, Zachary SO, Adaramola MS. A comprehensive review of maximum power point tracking (MPPT) techniques used in solar PV systems. Energies. 2023;16(5):2206. https://doi.org/10.3390/en16052206

[4] Mazumdar D, Sain C, Biswas PK, Sanjeevikumar P, Khan B. Overview of solar photovoltaic MPPT methods: a state of the art on conventional and artificial intelligence control techniques. International Transactions on Electrical Energy Systems. 2024;2024:8363342. https://doi.org/10.1155/2024/8363342

[5] Ahmed S, Mekhilef S, Mubin M, Tey KS, Kermadi K. An adaptive P&O algorithm with enhanced skipping feature for fast global maximum power point tracking under partial shading conditions. IEEE Transactions on Power Electronics. 2023;38(9):11601–11613. https://doi.org/10.1109/TPEL.2023.3285243

[6] Harrison A, Nfah EM, Ndongmo JDDN, Alombah NH. An enhanced P&O MPPT algorithm for PV systems with fast dynamic and steady-state response under real irradiance and temperature conditions. International Journal of Photoenergy. 2022;2022:6009632. https://doi.org/10.1155/2022/6009632

[7] Villegas-Mier CG, Rodriguez-Resendiz J, Alvarez-Alvarado JM, Rodriguez-Resendiz H, Herrera-Navarro AM, Rodriguez-Abreo O. Artificial neural networks in MPPT algorithms for optimization of photovoltaic power systems: a review. Micromachines. 2021;12(10):1260. https://doi.org/10.3390/mi12101260

[8] Zecevic Z, Rolevski M. Neural network approach to MPPT control and irradiance estimation. Applied Sciences. 2020;10(15):5051. https://doi.org/10.3390/app10155051

[9] Abouzeid AF, Eleraky H, Kalas A, Rizk R, Elsakka MM, Refaat A. Experimental validation of a low-cost maximum power point tracking technique based on artificial neural network for photovoltaic systems. Scientific Reports. 2024;14:18280. https://doi.org/10.1038/s41598-024-67306-0

[10] Eyimaya SE. Efficiency analysis of artificial intelligence and conventional maximum power point tracking methods in photovoltaic systems. Applied Sciences. 2025;15(10):5586. https://doi.org/10.3390/app15105586

[11] MathWorks. 250-kW grid-connected PV array. Simscape Electrical / Specialized Power Systems example documentation [Internet]. [cited 2026 Aug 25].

[12] OPAL-RT Technologies. 250-kW grid-connected PV array. SPS software documentation [Internet]. Updated 2026 [cited 2026 Aug 25].

[13] SunPower. SPR-415E-WHT-D photovoltaic module data, as documented in the adopted benchmark model.

[14] Fahim SR, Hasanien HM, Turky RA, Abdel Aleem SHE, Calasan M. A comprehensive review of photovoltaic modules models and algorithms used in parameter extraction. Energies. 2022;15(23):8941. https://doi.org/10.3390/en15238941

[15] Deotti LMP, da Silva Junior IC. A survey on the parameter extraction problem of the photovoltaic single diode model from a current–voltage curve. Solar Energy. 2023;263:111930. https://doi.org/10.1016/j.solener.2023.111930

[16] MathWorks. fitnet: function fitting neural network. Deep Learning Toolbox documentation [Internet]. [cited 2026 Aug 25].

[17] MathWorks. genFunction: generate MATLAB function for simulating shallow neural network. Deep Learning Toolbox documentation [Internet]. [cited 2026 Aug 25].

@article{1724544,
      author = {Ibe Precious Nnamdi},
      title = {Comparative Analysis of Perturb and Observe and Artificial Neural Network Maximum Power Point Tracking for a 250-kW Grid-Connected Photovoltaic System},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {5086-5095},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724544.pdf},
      abstract = {This paper examines the implementation of conventional Perturb and Observe (P&O) and feedforward artificial neural network (ANN) maximum power point tracking (MPPT) reference generators in a nominal 250-kW grid-connected photovoltaic (PV) system modelled in MATLAB/Simulink. Both controllers were considered within a common downstream architecture containing a DC-link voltage regulator, current regulator, pulse-width-modulation stage, three-level IGBT converter, transformer, and grid interface. The documented disturbance sequence reduced irradiance from 1000 to 200 W/m2 and restored it to 1000 W/m2 while cell temperature remained at 45 °C. The reference P&O plant operated at approximately 481 V and 236 kW at 1000 W/m2, and 464 V and 46 kW at 200 W/m2. Thus, an 80% irradiance reduction corresponded to an approximately 80.51% reduction in mean DC power and a 3.53% reduction in DC-link voltage. The ANN was fitted in MATLAB using five simulation-derived irradiance/reference-voltage points at 200, 400, 600, 800, and 1000 W/m2. Its archived low-irradiance cursor value was 46.66 kW, approximately 1.43% above the rounded 46-kW reference operating value. Because the 200-W/m2 point was included in fitting and the independent maximum-power trajectory and raw time-series signals were not retained, this result demonstrates reproduction of a fitted operating point rather than independent generalization. The available evidence therefore supports successful ANN integration as a proof of concept, but does not establish superiority over P&O in settling time, ripple, tracking efficiency, energy capture, or computational cost.},
      keywords = {photovoltaic systems; maximum power point tracking; Perturb and Observe; artificial neural network; grid-connected PV; MATLAB/Simulink},
      month = {October},
  }