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An assessment of series resistance estimation techniques for different silicon based SPV modules

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  • Singh, Rashmi
  • Sharma, Madhu
  • Rawat, Rahul
  • Banerjee, Chandan

Abstract

The series resistance is a significant electrical parameter of the solar photovoltaic module and is used for outdoor performance modeling, evaluation and degradation studies. There are a number of methods available in the literature to estimate series resistance and it's necessary to aggregate the available literature. In this paper, a comprehensive literature survey has been carried out, comprising of more than 33 different methods. These methods have been broadly classified into four groups on the basis of different approaches for the estimation of series resistance, i.e. numerical; graphical; a combination of numerical and graphical; and advanced methods. Further, a case study has been undertaken for carrying out a comparative analysis of ten different methods of series resistance for three different technologies namely, hetero-junction intrinsic thin layer silicon, amorphous single junction silicon and multi-crystalline silicon. The series resistance of three different solar photovoltaic technologies has been estimated using nine different analytical methods as well as IEC 60891 Edi 2.0 procedure using the current-voltage characteristics measured at various ranges of irradiance and temperature during outdoor exposure. Furthermore, the effect of irradiance and temperature on the series resistance has also been evaluated. It has been found that the analytical model provides a more accurate estimation of series resistance for multi-crystalline silicon and the computational model number three is found to be more accurate as compared to the other analytical models.

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  • 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.
    • Handle: RePEc:eee:rensus:v:98:y:2018:i:c:p:199-216
    DOI: 10.1016/j.rser.2018.09.020
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