In the intra-hour time scale, shielding of solar radiation by clouds is the main reason for the fluctuation of photovoltaic, so cloud parameters are important for intra-hour solar irradiance and photovoltaic power forecasting. Extracting cloud regions from cloud images provides a basis for quantifying cloud size and shape. It is difficult to detect clouds in these three cases, such as clouds in circumsolar region, clouds in haze weather and thin clouds at cloud edge. Aiming at such problems, this study firstly establishes a clear sky library based on pixel-level sun positions and haze conditions to deal with the complex changes of sky brightness. Then this study proposes a cloud detection method for ground-based images, which performs two segmentations on the cloud image. The initial segmentation process is a combination method that uses clear sky library method when the sun is visible and uses adaptive threshold method when the sun is occluded. The secondary segmentation process is based on superpixels and local threshold method, which restores some thin clouds that are easily ignored. Finally, for the influence of ghosts, this study summarizes the position and color features of ghosts, and uses different color channel information to deal with them.

中文翻译：

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基于晴空库和超像素局部阈值的地基天空图像云检测方法

在小时内时间尺度上，云层对太阳辐射的遮挡是光伏发电波动的主要原因，因此云参数对于小时内太阳辐照度和光伏发电功率预测具有重要意义。从云图像中提取云区域为量化云的大小和形状提供了基础。这三种情况是很难检测到云的，例如环太阳区云、雾霾天气云和云边薄云。针对此类问题，本研究首先建立基于像素级太阳位置和雾霾条件的晴空库，以应对天空亮度的复杂变化。然后，本研究提出了一种地基图像的云检测方法，该方法对云图像进行两次分割。初始分割过程是太阳可见时采用晴空库法，太阳被遮挡时采用自适应阈值法的组合方法。二次分割过程基于超像素和局部阈值方法，恢复了一些容易被忽略的薄云。最后，针对鬼影的影响，本研究总结了鬼影的位置和颜色特征，并利用不同的颜色通道信息对其进行处理。