Mining different over-limit patterns of the low voltage is very important to guide the management of low voltage issues in users. Due to the complexity and the ever-changing nature of voltage

the over-limit patterns of low voltage are always inherently unknown in users. A pattern mining method for low voltage in users based on hierarchical affinity propagation clustering (HAP) is proposed in this paper. Firstly

large-scale voltage data is clustered into several clusters using the HAP clustering algorithm

and these clusters are regarded as the different over-limit patterns of low voltage. Then

four indices are defined from two aspects of the duration and amplitude to characterize the features of the clusters. The features of the over-limit patterns are then drived by calculating the indices for each cluster. Finally

the proposed method is applied to a real dataset

effectively mining four over-limit patterns of low voltage. The characteristics of different patterns provide the important information for the supervision and analysis of low voltage issues in users

and the priorities of low voltage problems management in users can be well leveled.