【摘要】 开展用户低电压越限模式挖掘工作可以为用户低电压问题的治理提供指导。针对目前台区低电压用户电压复杂多变、低电压越限模式未知的问题，文中提出基于层次近邻传播(hierarchical affinity propagation, HAP)聚类的用户低电压越限模式挖掘方法。首先，通过HAP聚类算法对大规模低电压用户电压数据集进行聚类分析，获得若干聚类簇。然后，将不同的聚类簇视作不同的低电压越限模式，并从越限时长和越限电压幅值两方面定义低电压越限模式的4项基本特征指标，通过计算各聚类簇的基本特征指标，反映其所对应低电压越限模式的特征。最后，将该方法运用到某地区低电压用户的电压数据集中，有效挖掘出该地区低电压用户的4种低电压越限模式，从而根据不同低电压越限模式的特征，有针对性地开展低电压用户的监管、分析工作，并制定用户低电压问题治理的优先级。更多还原

【Abstract】 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.更多还原