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Partitioning Active Distribution Networks by Using Spectral Clustering

机译：使用频谱聚类来分区主动分配网络

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摘要

With the increasing intermittent renewable sources integrated into active distribution networks (ADNs), distributed management should be adopted to improve the control and management level of ADNs effectively. Accurate partitioning is a vital step to manage distributed resources. A partitioning method of ADNs by using spectral clustering algorithm is proposed in this paper to avoid the curse of dimensionality and accurately obtain the partitioning scheme of the ADNs. First, the graph model of the ADN is established to simplify the distribution network structure. Then the normalized symmetric Laplacian matrix of the distribution network is calculated according to the graph model. Finally, the eigenvectors of the normalized Laplacian matrix are solved and dimensionally reduced by using spectral clustering. The k-means algorithm is used to cluster the newly generated node data to obtain the partitions of the ADN. Case studies on the IEEE 13-node test feeder and IOWA 240-node distribution test system demonstrate the feasibility, adaptability, and efficacy of the proposed partitioning method.
机译：随着越来越多的间歇性再生能源集成到主动配送网络（ADNS）中，应采用分布式管理来改善adns的控制和管理水平。准确的分区是管理分布式资源的重要步骤。本文提出了一种利用光谱聚类算法的ADN的分区方法，以避免维度的诅咒，并准确地获得ADN的分区方案。首先，建立ADN的图形模型以简化分配网络结构。然后根据图模型计算分配网络的归一化对称Laplacian矩阵。最后，通过使用光谱聚类来解决归一化拉普拉斯基质的特征向量并尺寸减少。 K-means算法用于聚类新生成的节点数据以获得ADN的分区。对IEEE 13节点测试馈线和IOWA 240节点分布测试系统的案例研究证明了所提出的分区方法的可行性，适应性和功效。

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