基于社群特征的配电网异常用电行为分析

摘要

In order to solve the problem of poor accuracy, low efficiency, and high consumption of human resources in abnormal power consumption of power distribution network, this paper used data mining technology to accurately locate abnormal power consumption data in magnanimity power utilization data.The network users who might have non-technical loss (NTL) were analyzed and detected by using the industry's seasonal power consumption level of the community's habits and other abnormal classification indicators.The improved particle swarm LM neural network optimization algorithm was utilized to establishe an effective automatic recognition model for abnormal power consumption.The experimental results show that this model can effectively extract the electricity characteristics and realize the detection of abnormal users with strong recognition ability and high practicability.%针对目前配电网异常用电行为精度欠佳、效率低下、人力资源耗费量大等问题, 在海量用电数据中利用数据挖掘技术实现异常用电数据的精确查找与定位.通过引入社群习惯的行业季节用电水平等异常分类指标, 对可能存在非技术性损耗 (NTL) 的配网用户进行分析和检测, 利用改进粒子群LM神经网络算法建立了有效的异常用电行为的自动识别模型.实验结果表明:该模型能够有效地提取用电特征, 实现对异常用户的检测, 具有较强的识别能力和较高的实用性.