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Volume 18, Issue 1
  • ISSN: 2213-1116
  • E-ISSN: 2213-1132

Abstract

Background

The use of solar energy through photovoltaic arrays is continually expanding and has recently been regarded as one of the cleanest sources of energy. Increasing the amount of electricity provided to the load is one method for lowering the cost of solar systems. Contrarily, altering the load creates a divergence from the maximum power point (MPP) and changes the operating point of the solar conversion system.

Methods

Due to this, attention has been given to MPPT techniques that can be used with solar systems in numerous research investigations. In this paper, we implement an MPPT method based on an Artificial Neural Network (ANN). This method combined two controllers, ANN and fuzzy logic controller, under the name ANN-Fuzzy logic hybrid. In the first stage, ANN can determine Vmpp from irradiation and temperature, and both of them are variable. In the second stage, this Vmpp has been corrected by implementing Fuzzy logic in order to minimize the error of the voltage and VmPP.

Results

ANN-fuzzy hybrid has been simulated in Matlab-Simulink and was found to be the best solution to follow the maximum power point when irradiation and temperature are varied. The energy extracted from PV is delivered to a battery in order to inject this power into the smart grid by using an inverter controlled by PID. Finally, a LC filter has been used to eliminate the harmonics and compensate for the reactive power.

Conclusion

For energy storage, we consider the utilization of lithium-ion batteries, which are recognized as an optimal solution for storing energy efficiently. To manage the charge and discharge of the battery, we employ a PID controller and a buck-boost converter. Through this research, we aim to explore the performance and effectiveness of the hybrid ANN-fuzzy simulation for the boost converter system. By combining the capabilities of ANN and fuzzy logic, we expect to achieve improved control and optimization of the power conversion processes in the system. Additionally, the integration of lithium-ion batteries and the use of the PID controller and buck-boost converter allow for efficient management of energy storage and retrieval. Overall, this study investigates the hybrid ANN-fuzzy simulation for the boost converter system, highlighting the integration of various components and control techniques to enhance the performance and efficiency of the system.

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2025-01-01
2024-12-25

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References

  1. ZahediA. Solar photovoltaic (PV) energy; latest developments in the building integrated and hybrid PV systems.Renew. Energy200631571171810.1016/j.renene.2005.08.007
     [Google Scholar]
  2. SupriyaJ. RajashekarJ.S. A comprehensive review of various isolated dc-dc converters topologies associated with photovoltaic applications.Recent Adv. Electr. Electron. Eng.2022158595606
     [Google Scholar]
  3. YounessH. AhmedG. BenachairE. Machine learning- based smart irrigation monitoring system for agriculture applications using free and low-cost iot platform2022 International Conference on Microelectronics (ICM)202304-07 December 2022, Casablanca, Morocco.
     [Google Scholar]
  4. AhmedM. HarbiI. KennelR. HeldweinM.L. RodríguezJ. AbdelrahemM. Performance evaluation of pv model-based maximum power point tracking techniques.Electronics20221116256310.3390/electronics11162563
     [Google Scholar]
  5. YunE.J. ParkJ.T. YuC.G. An maximum power point tracking interface circuit for low-voltage DC-type energy harvesting sources.Bull. Electr. Eng. Inform.20221163108311810.11591/eei.v11i6.4124
     [Google Scholar]
  6. KumarJ.H. ChS.B. YugandharJ. Design and investigation of short circuit current based maximum power point tracking for photovoltaic system.Sci. Acad. Publisher Inter. J. Res. Rev. Electr. Comp. Eng.20111220465149
     [Google Scholar]
  7. UllahK. WangY. ZamanA. ImtiazH. AhmadS. KumarB. Maximum power point technique (MPPT) for PV system based on improved pert and observe (P&O) method with PI controller.Int Res J Eng Technol.2019612813819
     [Google Scholar]
  8. AlZubaidiA.A. KhaliqL.A. HamadH.S. MPPT implementation and simulation using developed P&O algorithm for photovoltaic system concerning efficiency.BEEI20221152460247010.11591/eei.v11i5.3949
     [Google Scholar]
  9. MerabetL. OuriciA. WafaT. Efficiency of perturb and observe mppt for pv system with boost converter.BEST: IJMITE2021814
     [Google Scholar]
  10. ChoudharyD. SaxenaA. Incremental conductance MPPT algorithm for pv system implemented using DC- DC buck and boost converter.Int. J. Eng. Res. Appl.201448123132
     [Google Scholar]
  11. KeridR. BounnahY. Modeling and parameter estimation of solar photovoltaic based MPPT control using EKF to maximize efficiency.Bull. Electr. Eng. Inform202211524912499
     [Google Scholar]
  12. MotahhirS. El HammoumiA. El GhzizalA. Photovoltaic system with quantitative comparative between an improved MPPT and existing INC and P&O methods under fast varying of solar irradiation.Energy Rep.2018434135010.1016/j.egyr.2018.04.003
     [Google Scholar]
  13. RoblesA.C. TabordaG.J. RodríguezA.O. Fuzzy logic based MPPT controller for a PV system.Energies20171012203610.3390/en10122036
     [Google Scholar]
  14. ZhuT. DongJ. LiX. DingS. A comprehensive study on maximum power point tracking techniques based on fuzzy logic control for solar photovoltaic systems.Front. Energy Res.2021972794910.3389/fenrg.2021.727949
     [Google Scholar]
  15. AhmedA. MuhammadA. A comparative study of DC-DC buck, boost, and buck-boost converters with proportional-integral, sliding mode, and fuzzy logic controllers.Recent Adv. Electr. Electron. Eng.202215
     [Google Scholar]
  16. AsifR.M. Ur RehmanA. Ur RehmanS. ArshadJ. HamidJ. Tariq SadiqM. TahirS. Design and analysis of robust fuzzy logic maximum power point tracking based isolated photovoltaic energy system.Eng. Rep.202029e1223410.1002/eng2.12234
     [Google Scholar]
  17. MessaltiS. HarragA. LoukrizA. A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation.Renew. Sustain. Energy Rev.20176822123310.1016/j.rser.2016.09.131
     [Google Scholar]
  18. Fousseyni ToureA. DaniokoF. DiourteB. Application of artificial neural networks for maximal power point tracking.Int. J. Sustain. Green Energy20211024010.11648/j.ijrse.20211002.12
     [Google Scholar]
  19. ParvanehM.H. KhorasaniP.G. A new hybrid method based on fuzzy logic for maximum power point tracking of Photovoltaic Systems.Energy Rep.202061619163210.1016/j.egyr.2020.06.010
     [Google Scholar]
  20. KumarA. KumarA. AroraR. Overview of genetic algorithm technique for maximum power point tracking (MPPT) of solar PV system.IJCA201532124
     [Google Scholar]
  21. WaniT.A. A review of fuzzy logic and artificial neural network technologies used for MPPT.TURCOMAT202112229122918
     [Google Scholar]
  22. RemoaldoD. JesusI. Analysis of a traditional and a fuzzy logic enhanced perturb and observe algorithm for the MPPT of a photovoltaic system.Algorithms20211412410.3390/a14010024
     [Google Scholar]
  23. RezkH. AlyM. Al-DhaifallahM. ShoyamaM. Design and hardware implementation of new adaptive fuzzy logic-based MPPT control method for photovoltaic applications.IEEE Access2019710642710643810.1109/ACCESS.2019.2932694
     [Google Scholar]
  24. DanandehM.A. MousaviS.M.G. A new architecture of INC-fuzzy hybrid method for tracking maximum power point in PV cells.Sol. Energy2018171692703
     [Google Scholar]
  25. NapoleC. DerbeliM. BarambonesO. Fuzzy logic approach for maximum power point tracking implemented in a real time photovoltaic system.Appl. Sci.20211113592710.3390/app11135927
     [Google Scholar]
  26. Mukesh KumarD.S.R.K. Comparison between IC and Fuzzy logic MPPT algorithm based solar PV system using boost converter.Int. J. Adv. Res. Electr201504064927493910.15662/ijareeie.2015.0406007
     [Google Scholar]
  27. NaW. ChenP. KimJ. An improvement of a fuzzy logic-controlled maximum power point tracking algorithm for photovoltic applications.Appl. Sci.20177432610.3390/app7040326
     [Google Scholar]
  28. YilmazU. KircayA. BorekciS. PV system fuzzy logic MPPT method and PI control as a charge controller.Renew. Sustain. Energy Rev.201881994100110.1016/j.rser.2017.08.048
     [Google Scholar]
/content/journals/eeng/10.2174/0123520965262688231128053950
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ANN-fuzzy Hybrid Control Strategy for MPPT of Grid-connected PV
Recent Patents on Electrical & Electronic Engineering (Formerly Recent Patents on Electrical Engineering) 18, 35; https://doi.org/10.2174/0123520965262688231128053950
/content/journals/eeng/10.2174/0123520965262688231128053950
/content/journals/eeng/10.2174/0123520965262688231128053950
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  • Article Type:     Research Article
Keyword(s): Artificial neural network MPPT controller; buck-boost; DC-AC inverter; fuzzy logic controller; MATLAB-Simulink; observation; Perturb; PID controller
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