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Series resistance determination and further characterization of c-Si PV modules

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  • Yordanov, Georgi Hristov
  • Midtgård, Ole-Morten
  • Saetre, Tor Oskar

Abstract

This paper presents a new algorithm for determination of the series resistance of crystalline-Si PV modules from individual illuminated I–V curves. The ideality factor and the reverse saturation current are then extracted in the classic way. The approach is applied to in-situ measured data from modules based on two types of mc-Si feedstock. The results indicate that the method yields physically meaningful parameters. An improved definition of local ideality factor is suggested, resulting in m-V plots unaffected by the series resistance. In addition, m-I plots are introduced for the first time. The novel differential techniques reveal an unexpected rise of the ideality factor at open circuit by about 5%.

Suggested Citation

  • Yordanov, Georgi Hristov & Midtgård, Ole-Morten & Saetre, Tor Oskar, 2012. "Series resistance determination and further characterization of c-Si PV modules," Renewable Energy, Elsevier, vol. 46(C), pages 72-80.
    • Handle: RePEc:eee:renene:v:46:y:2012:i:c:p:72-80
    DOI: 10.1016/j.renene.2012.03.012
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    References listed on IDEAS

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    1. Midtgard, Ole-Morten & Sætre, Tor Oskar & Yordanov, Georgi & Imenes, Anne Gerd & Nge, Chee Lim, 2010. "A qualitative examination of performance and energy yield of photovoltaic modules in southern Norway," Renewable Energy, Elsevier, vol. 35(6), pages 1266-1274.
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    Cited by:

    1. Lim, Li Hong Idris & Ye, Zhen & Ye, Jiaying & Yang, Dazhi & Du, Hui, 2015. "A linear method to extract diode model parameters of solar panels from a single I–V curve," Renewable Energy, Elsevier, vol. 76(C), pages 135-142.
    2. 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.
    3. Ma, Tao & Yang, Hongxing & Lu, Lin, 2014. "Solar photovoltaic system modeling and performance prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 304-315.
    4. A. Bassam & O. May Tzuc & M. Escalante Soberanis & L. J. Ricalde & B. Cruz, 2017. "Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System," Sustainability, MDPI, vol. 9(8), pages 1-16, August.

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