The problem of distribution network operation optimization is diversified and uncertain. In order to solve this problem, this paper proposes a method of distribution network operation optimization considering wind-solar clustering, which includes source load and storage. Taking the total operating cost as the objective function, it includes network loss cost, unit operating cost, and considers a variety of constraints such as energy storage device constraints and demand response constraints. This paper aims to optimize the operation according to different wind-solar clustering scenes to improve the economy of distribution network. Taking the 365-day wind-solar output curves as the research object, K-means clustering is carried out, and the best k value is obtained by elbow rule. The second-order cone programming method and solver are used to solve the optimization model of each typical scenario, and the operation optimization analysis of each typical scenario obtained by clustering is carried out. Taking IEEE33 system and local 365-day wind-solar units output scenes as examples, the period is 24 h, which verifies the effectiveness of the proposed method. The proposed method has guiding significance for the operation optimization of distribution network.

Abstract:The color feature has been taken an important role in color image segmentation, especially in the fields of automatic detection of crop disease based on leaf image. In this paper an effective method for image segmentation of cucumber leaf images is proposed. First, the color space model is analyzed. Then a kind of color feature is applied to obtain the feature map, which combines RGB model and HSI model. Finally, the morphological method is used to accomplish the image segmentation. This method has been shown effective through experiments.crop disease, color image segmentation, feature extraction, color space

The large-scale grid connection of new energy will affect the optimization of power flow. In order to solve this problem, this paper proposes a power flow optimization strategy model of a distribution network with non-fixed weighting factors of source, load and storage. The objective function is the lowest cost, the smallest voltage deviation and the smallest power loss, and many constraints, such as power flow constraint, climbing constraint and energy storage operation constraint, are also considered. Firstly, the equivalent load curve is obtained by superimposing the output of wind and solar turbines with the initial load, and the best k value is obtained by the elbow rule. The k-means algorithm is used to cluster the equivalent load curve in different periods, and then the fuzzy comprehensive evaluation method is used to determine the weighting factor of the optimization model in each period. Then, the particle swarm optimization algorithm is used to solve the multi-objective power flow optimization model, and the optimal strategy and objective function values of each unit output in the operation period are obtained. Finally, IEEE33 is used as an example to verify the effectiveness of the proposed model through two cases: a fixed proportion method to determine the weighting factor, and this method to determine the weighting factor. The proposed method can improve the economy and reliability of distribution networks.

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