Cloud detection for ground-based sky images has attracted much attention in cloud-related fields. In this paper, we proposed a cloud detection algorithm that reduced the sunlight interference in the image. The solar location method was introduced to track the sun in the image used for feature calculation, which was suitable for the case where the camera could not be calibrated. Following this, the adjustable red green difference (ARGD) feature using red and green channels was proposed. The red weight in the feature was determined by the layering region division, which classified the degree of sunlight interference in the image, and the sky state, which discriminated whether there was sunlight interference in the image. Finally, a fixed zero threshold was applied to feature images in order to obtain the cloud detection results. Experimental results showed that the proposed algorithm performs better than the other algorithms and can effectively reduce the sunlight interference.

A substation planning method that accounts for the widespread introduction of distributed generators (DGs) in a low-carbon economy is proposed. With the proliferation of DGs, the capacity that DGs contribute to the distribution network has become increasingly important. The capacity of a DG is expressed as a capacity credit (CC) that can be evaluated according to the principle that the reliability index is unchanged before and after the introduction of the DG. A method that employs a weighted Voronoi diagram is proposed for substation planning considering CC. A low-carbon evaluation objective function is added to the substation planning model to evaluate the contribution of DGs to a low-carbon economy. A case study is analyzed to demonstrate the practicality of the proposed method.

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