One Diode PV Modeling Under Varying Irradiance

Christopher Teh Jun Qian, M. Drieberg, S. Soeung
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Abstract

Internet of Things (IoT) is a massive network of connected devices that enables data sharing and analysis for extracting valuable information. Many industries have started to integrate IoT into their devices to increase their businesses’ competitiveness. IoT devices which consume less power, can be potentially powered up using an energy harvesting system instead of batteries. A photovoltaic (PV) panel converts light energy into electrical energy is used to harvest the power. To predict the behaviour of PV panel, an accurate model is required. Most of the manufacturers provide values of three characteristic points (open circuit point, short circuit point, and maximum power point) at standard test conditions (STC) condition. However, STC condition is not always achieved in reality. Therefore, this paper presents the methodology for modeling an accurate one diode model with two resistors under different irradiance with the help of characteristic points translation technique. The proposed model is applied on a commercial PV panel. Three characteristic points of the model are obtained and validate with the datasheet values. The results achieve a good agreement with a difference below than 5 %. The proposed model shows an accuracy improvement when compared to the existing models.
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变辐照度下的单二极管PV模型
物联网(IoT)是一个由连接设备组成的庞大网络,可以实现数据共享和分析,以提取有价值的信息。许多行业已经开始将物联网集成到他们的设备中,以提高企业的竞争力。物联网设备消耗更少的电力,可以使用能量收集系统而不是电池供电。光伏(PV)面板将光能转换为电能,用于收集电力。为了预测光伏板的性能,需要一个精确的模型。大多数制造商在标准测试条件(STC)条件下提供三个特征点(开路点,短路点和最大功率点)的值。然而,在现实中,并非总能达到STC条件。因此,本文提出了利用特征点平移技术在不同辐照度下精确建模具有两个电阻的单二极管模型的方法。将该模型应用于商用光伏板。得到了模型的三个特征点,并与数据表值进行了验证。结果吻合较好,误差小于5%。与现有模型相比,该模型的精度得到了提高。
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