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Image Processing-based Assessment of Dust Accumulation on Photovoltaic Modules

机译:基于图像处理的光伏组件上灰尘积聚的评估

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Numerous environmental factors significantly affect the energy yield of solar photovoltaic (PV) power plants. Among these, solar irradiance, photovoltaic module temperature, dust and shading are prominent. The level of soiling is directly related to the installation site of the PV plant. In this study, to investigate the impact of dust shading factor on energy efficiency, artificial light source in laboratory environment is used and power outputs are compared for three different densities of dust accumulation on the module surface. For each level of dust accumulation, images are obtained from PV modules. From the PV module images obtained by a camera for different levels of dust accumulation, new features are obtained based on Gray Level Co-occurrence Matrix. The obtained data with new features are classified on the basis of Artificial Neural Networks to determine dust level and its effect on PV module performance.
机译:许多环境因素都会严重影响太阳能光伏(PV)电厂的能源产量。其中,太阳辐照度,光伏组件温度,灰尘和阴影尤为突出。污染程度与光伏电站的安装地点直接相关。在这项研究中,为了研究灰尘遮蔽因子对能源效率的影响,使用了实验室环境中的人造光源,并比较了模块表面上三种不同密度的灰尘堆积的功率输出。对于每个级别的灰尘积累,都可以从PV模块获得图像。从相机获得的用于不同粉尘积累水平的PV模块图像中,基于灰度共生矩阵获得新功能。根据人工神经网络对获得的具有新功能的数据进行分类,以确定粉尘水平及其对光伏组件性能的影响。

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