Power big data mainly comes from all aspects of power generation

transmission

transformation

distribution

power consumption and dispatching of power production and energy use. How to use these data to improve the intelligent level of power management has become one of the most important research topics of the related power links. However

the existing clustering methods used in power big data can not find clusters of arbitrary shape

which affects the calculation accuracy and calculation time in the application to power big data. A new algorithm is proposed

which uses the local density peak and the distance based on shared neighbor points to better combine the relationship between density and distance and express the differences between data. The minimum spanning tree (MST) is constructed by using the local density peak and the distance based on the shared neighbor

and then the longest edge is cut repeatedly until a given number of clusters is found. The experimental results show that the proposed algorithm has a good effect in the application to power big data.