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An efficient method for predicting PV modules performance based on the two-diode model and adaptable to the single-diode model

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  • Tifidat, Kawtar
  • Maouhoub, Noureddine

Abstract

This paper introduces a simplified and accurate modeling approach to model photovoltaic modules. The current method uses a hybrid technique combining numerical and analytical approaches to predict the current-voltage characteristics of PV generators by calculating the parameters of the double-diode model. First, to reduce the complexity of the current equation and minimize the calculation time, some simplifications reducing the research space from seven to only five unknowns are taken into consideration. Second, three of these five parameters are extracted using simple analytical equations derived from the output current's equation based on the available values of the typical points on the manufacturer's datasheet. Then, the remaining two parameters are extracted using a combination of numerical approach and slope adjustment technique at the short-circuit point. The technique is adapted to the single-diode model in order to study the effect of the second diode on the simulating accuracy. The effectiveness is tested over six PV modules matching different PV technologies, and the results are compared with a great number of modeling methods based on different approaches, and the adjustment technique has shown the highest accuracy levels (RMSE values less than 0.045A) at the least computational times (compilation time less than 0.084 s).

Suggested Citation

  • Tifidat, Kawtar & Maouhoub, Noureddine, 2023. "An efficient method for predicting PV modules performance based on the two-diode model and adaptable to the single-diode model," Renewable Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:renene:v:216:y:2023:i:c:s0960148123010169
    DOI: 10.1016/j.renene.2023.119102
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    References listed on IDEAS

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    1. Orioli, Aldo & Di Gangi, Alessandra, 2019. "A procedure to evaluate the seven parameters of the two-diode model for photovoltaic modules," Renewable Energy, Elsevier, vol. 139(C), pages 582-599.
    2. Yu, Kunjie & Liang, J.J. & Qu, B.Y. & Cheng, Zhiping & Wang, Heshan, 2018. "Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models," Applied Energy, Elsevier, vol. 226(C), pages 408-422.
    3. Kawtar Tifidat & Noureddine Maouhoub & Abdelaaziz Benahmida, 2022. "A New Reduced Form for Real-Time Identification of PV Panels Operating Under Arbitrary Conditions," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 11(2), pages 1-23, April.
    4. Makrides, George & Zinsser, Bastian & Phinikarides, Alexander & Schubert, Markus & Georghiou, George E., 2012. "Temperature and thermal annealing effects on different photovoltaic technologies," Renewable Energy, Elsevier, vol. 43(C), pages 407-417.
    5. Arabshahi, M.R. & Torkaman, H. & Keyhani, A., 2020. "A method for hybrid extraction of single-diode model parameters of photovoltaics," Renewable Energy, Elsevier, vol. 158(C), pages 236-252.
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