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Maximum power point tracking controller with online adaptive reference voltage generator for disturbance rejection

Author

Listed:
  • Nagi, Farrukh
  • Kumaran, Vigna
  • Mansor, M.
  • Verayiah, Renuga
  • Mohamed, Hassan Bin
  • Permal, Navinesshani

Abstract

This paper presents a simple adaptive reference voltage generator for maximum power point tracking controller for environmental and internal disturbances. Internal load and line disturbances in electrical systems cause voltage and frequency fluctuations and instability in standalone or low-voltage (LV) grid-connected PV systems. Efficient adaptive Reference voltage has been used previously with offline recorded data, which is difficult for varying environmental and load–line disturbances, resulting in unstable power transfer. The proposed Maximum Power Point Voltage controller (VmppC) generates online maximum power point reference voltage with light and temperature sensors. The VmppC evaluates the reference voltage using the PV datasheet with the single-diode PV model's recursive equation solution. The duty cycle evaluated from the proposed controller generates the PWM for switching the boost converter according to the varying load and line disturbances, as sensed by the PV panel. The comparison of the proposed controller is made with two popular Variable Step Size (VSS) Perturb & observe (VSS-P&O) and Incremental Conductance (VSS-INC) techniques known for maximum efficiency under varying environmental conditions. The simulation results show that the proposed VmppC performs better than P&O and INC in eliminating environmental and internal disturbances.

Suggested Citation

  • Nagi, Farrukh & Kumaran, Vigna & Mansor, M. & Verayiah, Renuga & Mohamed, Hassan Bin & Permal, Navinesshani, 2025. "Maximum power point tracking controller with online adaptive reference voltage generator for disturbance rejection," Renewable Energy, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:renene:v:241:y:2025:i:c:s0960148124023450
    DOI: 10.1016/j.renene.2024.122277
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    References listed on IDEAS

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    1. Singh, Rashmi & Sharma, Madhu & Yadav, Kamlesh, 2022. "Degradation and reliability analysis of photovoltaic modules after operating for 12 years: A case study with comparisons," Renewable Energy, Elsevier, vol. 196(C), pages 1170-1186.
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    Cited by:

    1. Yujie Chen & Jianan Wang & Lele Peng & Jiachen Qiao, 2025. "A Novel Maximum Power Point Inference Method for Distributed Marine Photovoltaic Monitoring," Energies, MDPI, vol. 18(11), pages 1-23, May.

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