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A Comparative Analysis of Different Maximum Power Point Tracking Algorithms of Solar Photovoltaic System

机译:太阳能光伏系统不同最大功率点跟踪算法的比较分析

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The demand for power is increasing day by day, and working with fossil fuels is associated with global warming problem, so renewable resource is better option to fulfil ever-increasing power demand without affecting climate. Among renewable resources, solar energy is widely adopted due to its availability in abundance. The power output for a solar photovoltaic (SPV) cell depends on the operating temperature, solar irradiation and load impedance. So maximum power point of SPV is not constant and keeps on changing under different conditions. Therefore, maximum power point tracking is used to uphold the point to extract maximum power from PV system under different working conditions. In this paper, we have described many maximum power point tracking algorithms commonly used for extracting maximum power out of a solar panel. We have also done a comparative analysis among them for various parameters using a common Simulink model in MATLAB. It has been found that among different methods, artificial intelligence method is most efficient compared to all existing methods and perform well under different conditions of solar irradiations and temperatures.
机译:对电力的需求日益增加,并使用化石燃料与全球变暖问题有关,因此可再生资源更好地选择不影响气候的不断增长的功率需求。在可再生资源中,由于其丰富的可用性,太阳能被广泛采用。太阳能光伏(SPV)电池的功率输出取决于操作温度,太阳照射和负载阻抗。因此,SPV的最大功率点不是恒定的,并且在不同条件下不断变化。因此,最大功率点跟踪用于维持在不同的工作条件下从PV系统提取最大功率的点。在本文中,我们描述了许多最大功率点跟踪算法,用于提取太阳能电池板的最大电源。我们还在Matlab中使用公共Simulink模型进行了各种参数的比较分析。已经发现,与所有现有方法相比,人工智能方法最有效,并且在太阳照射和温度的不同条件下表现良好。

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