论文标题
太阳辐照度预测的数据获取和图像处理
Data acquisition and image processing for solar irradiance forecasting
论文作者
论文摘要
由太阳能提供动力的微电网（MG）中可用的能量与生成时刻的天气条件密切相关。太阳辐照度的非常短期的预测为MG提供了自动控制能源调度的能力。为此，我们提出了一种从长软件红外（IR）图像中提取的云特征统计量化的方法，以预测透明天空指数（CSI）。使用安装在太阳能跟踪器上的数据采集系统（DAQ）获得图像。我们解释了如何消除由DAQ中设备引起的数据中的环溶性偏差。我们研究了一种在全球水平辐照度（GHI）测量下降低后获得CSI的方法。我们提出了一种融合多个托管可见（VI）光图像的方法。我们实现了一种使用IR摄像机的辐射测量值提取物理特征的方法。我们引入了一个模型，以从IR图像中删除大气散射辐射的效果以及太阳直接辐射的效果。我们解释了如何模拟IR相机窗口的扩散辐射，这是由水点和灰尘颗粒堆叠到DAQ封闭式锗镜头的。使用大气条件模型选择了用于建模相机窗口的框架。该模型将天空分类为四个不同的类别：清除，积云，Stratus和Nimbus。我们将像素大小的几何变换引入了它们在给定高度的大气平面上的实际维度。根据摄像机的阳光和视场（FOV）的高度角度执行此转换。我们比较了转换的转换和隔离症之间的误差。
The energy available in Micro Grid (MG) that is powered by solar energy is tightly related to the weather conditions in the moment of generation. Very short-term forecast of solar irradiance provides the MG with the capability of automatically controlling the dispatch of energy. To achieve this, we propose a method for statistical quantification of cloud features extracted from long-ware infrared (IR) images to forecast the Clear Sky Index (CSI). The images are obtained using a data acquisition system (DAQ) mounted on a solar tracker. We explain how to remove cyclostationary bias in the data caused by the devices in the own DAQ. We investigate a method to obtain the CSI, after the detrending of Global Horizontal Irradiance (GHI) measurements. We propose a method to fusion multiple exposures of circumsolar visible (VI) light images. We implement a method for extracting physical features using radiometric measurements of the IR camera. We introduce a model to remove from IR images both the effect of the atmosphere scatter radiation, and the effect of the Sun direct radiation. We explain how to model of diffuse radiation of the IR camera window, which is produce by water spots and dust particles stack to the germanium lens of the DAQ enclosure. The frames, that were used to model the camera window, are selected using an atmospheric condition model. This model classifies the sky four different categories: clear, cumulus, stratus, and nimbus. We introduce a geometric transformation of the size of the pixels to their actual dimension in a plane of the atmosphere which is at a given height. This transformation is performed according to the elevation angle of the Sun and field of view (FOV) of the camera. We compare the error between the transformation and anapproximation of transformation.
