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A new approach to select an optimal PV module model under the outdoor conditions

机译:在室外条件下选择最佳光伏组件模型的新方法

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Despite the great development in modeling the I-V characteristics of the PV module, the outdoor conditions variations still the main difficulty to predict its performances. In this work, anew approach proposed to reconstruct the I-V characteristic of a PV module under various real conditions of irradiance and temperature. Based on the characterization tests data, carried out on four different PV modules technologies (monocrystalline silicon, polycrystalline silicon, thin film CIS and amorphous silicon), under semi-arid environment conditions of Ghardaïa site, a developed methodology has been presented. It consists of exploiting a set of the five parameters data versus irradiance and temperature obtained via the five parameters model. The assembled data have been reconstructed via analytical and adaptive neuro-fuzzy inference system (ANFIS) models, for each PV module technology. The reconstructed of five parameters model obtained by ANFIS model give a good precision for all tested PV module types, in comparison to the analytical model.
机译:尽管在对光伏模块的I-V特性进行建模方面取得了长足的发展,但室外条件的变化仍然是预测其性能的主要困难。在这项工作中,提出了一种新方法来重构在各种实际辐照度和温度条件下的光伏组件的I-V特性。根据在四种不同的光伏组件技术(单晶硅,多晶硅,薄膜CIS和非晶硅)上进行的特性测试数据,在Ghardaïa场地的半干旱环境条件下,提出了一种开发的方法。它包括利用五个参数数据中的一组与通过五个参数模型获得的辐照度和温度进行比较。对于每种光伏组件技术,已通过分析和自适应神经模糊推理系统(ANFIS)模型重建了组装数据。与分析模型相比,通过ANFIS模型获得的五个参数模型的重构为所有测试的光伏组件类型提供了良好的精度。

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