Scholarlink

In this paper, we have proposed a new analytical approach to predict the real-time maximum power of PV (photovoltaic) modules operating outside under various temperature and irradiation conditions. Explicit model with one shaped parameter is used to model the behavior of the PV panel. Three analytical models of short-circuit current, open-circuit voltage and shape parameter as a function of temperature module and irradiation level have been proposed to predict I-V curves and the maximum power. To validate our predictive approach, experimental data from three solar panels of different technologies collected by NREL laboratory at the cocoa site in Florida have been used. The measured data of a reference day were used to optimize the various parameters of the three analytical models. Next, the approach is applied to forecast maximum power of two other selected days. Furthermore, the predictive and measured maximum power values are in good agreement and the normalized error has not exceeded 3%.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Performance Prediction of PV Modules Operating Outdoor Under Varying Conditions Based on Explicit Model

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Fatima Ezzahra Ait Salah,
  • Noureddine Maouhoub,
  • Kawtar Tifidat

摘要

In this paper, we have proposed a new analytical approach to predict the real-time maximum power of PV (photovoltaic) modules operating outside under various temperature and irradiation conditions. Explicit model with one shaped parameter is used to model the behavior of the PV panel. Three analytical models of short-circuit current, open-circuit voltage and shape parameter as a function of temperature module and irradiation level have been proposed to predict I-V curves and the maximum power. To validate our predictive approach, experimental data from three solar panels of different technologies collected by NREL laboratory at the cocoa site in Florida have been used. The measured data of a reference day were used to optimize the various parameters of the three analytical models. Next, the approach is applied to forecast maximum power of two other selected days. Furthermore, the predictive and measured maximum power values are in good agreement and the normalized error has not exceeded 3%.