基于数据挖掘技术的低压台区线损检测研究
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引用本文:程 慧,王镜芳 ,胡程平,吴方舟,刘爱禹,钱启宇.基于数据挖掘技术的低压台区线损检测研究[J].计算技术与自动化,2021,(2):61-65
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程 慧,王镜芳 ,胡程平,吴方舟,刘爱禹,钱启宇 (国网浙江海宁市供电有限公司浙江 海宁 314400) 
中文摘要:针对低压台区变压器端采集的电力数据量庞大难以分析线路损耗问题,深入分析了电力信息系统营销自动化过程中积累的海量用户数据,建立了合理且高效的线损分析数学模型。通过挖掘智能电表中这些数据背后的有用信息,实现对用户异常用电行为的检测,达到防止窃电和漏电的目的,从而降低线路损耗。利用加权LOF算法数据挖掘技术的电力线路窃电层次检测方法,可以对海量用户数据进行加权异常值分析,更有效地完成异常电力用户的定位。
中文关键词:线路损耗  数据挖掘  层次分析  加权LOF算法  异常值检测
 
Research on Line Loss Diagnosis of Low Voltage Station Based on Data Mining Technology
Abstract::In view of the huge amount of power data collected at the transformer end of the low-voltage substation area, it is difficult to analyze the line loss.The massive user data accumulated in the process of power information system marketing automation is analyzed in depth, and a reasonable and efficient line loss analysis mathematical model is established. By mining the useful information behind these data in the smart meter, we can detect the user's abnormal electricity use behavior, and achieve the purpose of preventing electricity theft and leakage, so as to reduce the line loss. Using the weighted LOF algorithm data mining technology of power line power stealing layered detection method, we can analyze the massive user data weighted outliers, and more effectively complete the positioning of abnormal power users.
keywords:line loss  data mining  AHP  weighted lof algorithm  outlier detection
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