In response to the problem of data diversity and high dimensionality caused by the large-scale integration of new energy into the distribution network

traditional clustering algorithms are unable to cope. A typical scenario generation method for distribution network based on multi-source data is proposed

and an evaluation index system for long short-term safe operation of distribution network is defined. Principal component analysis is used to reduce the dimensionality of high-dimensional data in the distribution network

and a two-stage clustering model based on density-based spatial clustering of applications with noise(DBSCAN) and improved K-means is established to obtain a typical scenario set that preserves extreme scenarios. Based on the safety operation elements of the distribution network

it defines the long short-term safety operation index system of the distribution network. An improved IEEE 33-node distribution network is simulated and compared with various clustering algorithms to verify the effectiveness and efficiency of the two-stage clustering algorithm. The typical scenarios obtained are applied to identify the weak points of voltage violation

which has high efficiency and accuracy.