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Photovoltaic module series resistance identification at its maximum power production

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  • Lappalainen, Kari
  • Piliougine, Michel
  • Valkealahti, Seppo
  • Spagnuolo, Giovanni

Abstract

Analysis of measured current–voltage curves offers a cost-effective option for online condition monitoring of photovoltaic (PV) modules. The current–voltage curves of PV modules can be modeled accurately using the well-known electrical single-diode model. In practical applications, condition monitoring should be based on measurements performed near the maximum power point (MPP) by affecting PV power production negligibly. The series resistance is the most important single-diode model parameter in assessing the condition of PV modules; this paper proposes a novel method for its determination by using measurements acquired near the MPP only. The proposed method can be used with any series resistance identification procedure based on current–voltage curve measurements. The proposed method is experimentally validated using current–voltage curves of two PV modules measured in Malaga, Spain. This study allows to assess that the series resistance can be accurately determined from measurements performed near the MPP. Especially the results obtained with an ISOFOTON ISF-145 PV module are very promising: the scaled series resistances obtained from measurements done without lowering the PV power more than 2% of the maximum power differ on the average by no more than 2% of the series resistances obtained from the whole current–voltage curves.

Suggested Citation

  • Lappalainen, Kari & Piliougine, Michel & Valkealahti, Seppo & Spagnuolo, Giovanni, 2024. "Photovoltaic module series resistance identification at its maximum power production," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PA), pages 50-62.
    • Handle: RePEc:eee:matcom:v:224:y:2024:i:pa:p:50-62
    DOI: 10.1016/j.matcom.2023.05.021
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    References listed on IDEAS

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    1. Singh, Rashmi & Sharma, Madhu & Rawat, Rahul & Banerjee, Chandan, 2018. "An assessment of series resistance estimation techniques for different silicon based SPV modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 199-216.
    2. Heidi Kalliojärvi & Kari Lappalainen & Seppo Valkealahti, 2022. "Feasibility of Photovoltaic Module Single-Diode Model Fitting to the Current–Voltage Curves Measured in the Vicinity of the Maximum Power Point for Online Condition Monitoring Purposes," Energies, MDPI, vol. 15(23), pages 1-21, November.
    3. Huerta Herraiz, Álvaro & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure," Renewable Energy, Elsevier, vol. 153(C), pages 334-348.
    4. Khan, Firoz & Baek, Seong-Ho & Kim, Jae Hyun, 2016. "Wide range temperature dependence of analytical photovoltaic cell parameters for silicon solar cells under high illumination conditions," Applied Energy, Elsevier, vol. 183(C), pages 715-724.
    5. Nader Anani & Haider Ibrahim, 2020. "Adjusting the Single-Diode Model Parameters of a Photovoltaic Module with Irradiance and Temperature," Energies, MDPI, vol. 13(12), pages 1-17, June.
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