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Innovative and precise MPP estimation using P–V curve geometry for photovoltaics

Author

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  • Kumar, Gaurav
  • Trivedi, Milind B.
  • Panchal, Ashish K.

Abstract

This paper elaborates a direct maximum power point (MPP) finding method for solar photovoltaics (PV) based on quadratic regression analysis of the geometry of the power–voltage (P–V) curve of a typical PV cell or module. This method works in two stages for determination of the MPP parameters such as voltage (Vmp), power (Pmp) and fill factor with high level of accuracy. At first, it determines the approximate MPP parameters using a few data collected from the open-circuit and short-circuit regions of a current–voltage (I–V) characteristic, and further it refines the obtained parameters using quadratic regression analysis. This method is non-iterative and requires no prior knowledge of the physical and electrical parameters of the cell. Besides high accuracy, the method is also very precise in handling the noise level (up to 20%) in the data. The method was tested on a wide range of PV cells reported in the literature including silicon, copper indium gallium selenide (CIGS), copper zinc tin sulphide selenide (CZTSSe) and organic cells. The estimated MPP parameters are in excellent agreement with those of reported for the cells. The method is also employed for an experimental P–V characteristic of a 10 Wp silicon module and a synthesized P–V characteristic of a 120 Wp silicon module. The estimated fill factor for silicon modules exactly matches with those available in the datasheet specifications. Experimental evaluation of the method as compared to perturb-and-observe (P&O) in the passive mode exhibits quick response. Thus, the method is applicable for a wide category of PV cells to PV modules.

Suggested Citation

  • Kumar, Gaurav & Trivedi, Milind B. & Panchal, Ashish K., 2015. "Innovative and precise MPP estimation using P–V curve geometry for photovoltaics," Applied Energy, Elsevier, vol. 138(C), pages 640-647.
    • Handle: RePEc:eee:appene:v:138:y:2015:i:c:p:640-647
    DOI: 10.1016/j.apenergy.2014.10.041
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    References listed on IDEAS

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    1. Patel, Sanjaykumar J. & Panchal, Ashish K. & Kheraj, Vipul, 2014. "Extraction of solar cell parameters from a single current–voltage characteristic using teaching learning based optimization algorithm," Applied Energy, Elsevier, vol. 119(C), pages 384-393.
    2. Ishaque, Kashif & Salam, Zainal & Shamsudin, Amir & Amjad, Muhammad, 2012. "A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 99(C), pages 414-422.
    3. Salam, Zainal & Ahmed, Jubaer & Merugu, Benny S., 2013. "The application of soft computing methods for MPPT of PV system: A technological and status review," Applied Energy, Elsevier, vol. 107(C), pages 135-148.
    4. Eltawil, Mohamed A. & Zhao, Zhengming, 2013. "MPPT techniques for photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 793-813.
    5. Bhatnagar, Pallavee & Nema, R.K., 2013. "Maximum power point tracking control techniques: State-of-the-art in photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 224-241.
    6. Kumar, Gaurav & Panchal, Ashish K., 2014. "Geometrical prediction of maximum power point for photovoltaics," Applied Energy, Elsevier, vol. 119(C), pages 237-245.
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    Cited by:

    1. He, Wei & Wang, Yang & Shaheed, Mohammad Hasan, 2015. "Maximum power point tracking (MPPT) of a scale-up pressure retarded osmosis (PRO) osmotic power plant," Applied Energy, Elsevier, vol. 158(C), pages 584-596.
    2. Marco Balato & Carlo Petrarca, 2020. "The Impact of Reconfiguration on the Energy Performance of the Distributed Maximum Power Point Tracking Approach in PV Plants," Energies, MDPI, vol. 13(6), pages 1-19, March.
    3. Marco Balato & Annalisa Liccardo & Carlo Petrarca, 2020. "Dynamic Boost Based DMPPT Emulator," Energies, MDPI, vol. 13(11), pages 1-16, June.
    4. Mohamed Louzazni & Daniel Tudor Cotfas & Petru Adrian Cotfas, 2020. "Management and Performance Control Analysis of Hybrid Photovoltaic Energy Storage System under Variable Solar Irradiation," Energies, MDPI, vol. 13(12), pages 1-23, June.

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