面 向 负 荷 特 征 分 析 的 地 理 分 布 式 协 同 聚 类 方 法

Under the framework of Energy Internet. The transmission delay of user data and management requirements of power grid companies promote the establishment of power data centers all over the country. Therefore, the power data naturally presents geographic distribution. In this regard, the clustering algorithm of user load characteristics in geo-distributed scenarios is studied. Firstly, for the characteristic analysis of user load in geographical nodes, based on the principal component analysis (PCA)-load index feature weighted combination algorithm, a K-means algorithm considering the density peak information is proposed, which provides the support for information consensus among geographical nodes. Secondly, according to the geo-distributed network perception structure of user requirement, the framework of distributed clustering model considering feature migration is constructed, and the distributed K-means algorithm is proposed to obtain the global clustering model by using the local information of nodes through parameter consensus, which realizes the global clustering of user characteristics on the premise that only public information is transmitted between nodes. Thus, the global clustering of user characteristics is realized. Finally, the model and algorithm are verified by user load data from the cities in Ireland and northern China. The results show that distributed K-means can use global information and consider differences in different regions to better identify typical power consumption curves, and the algorithm has better transferability.