摘要
自动化检定流水线为智能电能表的正常运行提供保障,然而流水线在长期运行中会发生性能退化甚至故障,尤其是表位机械环节的形变与锈蚀,会导致误差试验结果出现偏差。目前的人工定期检测方法无法及时响应流水线运维间隔中出现的异常工况,因此,实现自动化检定流水线表位异常的在线检测,具有重要意义。文章提出了一种智能电能表自动化检定流水线表位在线异常检测方法,通过对表位检定数据分布进行特征提取,将表位异常状态转换为数据分布的异常;并借助局部异常因子算法量化分布的异常程度,标记产生异常分布的表位;应用文章提出的方法对山东省电力公司计量中心智能电能表检定数据进行了分析,对比人工检查结果,验证了方法的有效性。
Automatic verification assembly line provides guarantee for the normal operation of smart meters.However,performance degradation or even failure may occur in the long-term operation process of assembly line,especially the deformation or rust of the mechanical link of the meter positions,resulting in deviation of the error test results.At present,the manual detection carried out regularly is unable to respond to the abnormal working conditions occurring in the maintenance interval in time.Therefore,it is of great significance to implement the online detection of the abnormal meter positions in the assembly line.This paper proposes a method for online anomaly detection of meter positions in automatic verification assembly line of smart meter,extracts the characteristics of the verification data distribution of the meter positions,and converts the abnormal state of the meter positions into the abnormality of data distribution.Then,the local outlier factor algorithm is adopted to quantify the abnormal degree of distribution,and the meter positions with abnormal distribution will be marked.Finally,this paper analyzes the smart meter verification data of the Metrology Center of Shandong Electric Power Company,and the results show that the proposed method is effective.
作者
邢宇
鲍志威
孙艳玲
李红斌
陈勉舟
焦洋
Xing Yu;Bao Zhiwei;Sun Yanling;Li Hongbin;Chen Mianzhou;Jiao Yang(Electric Power Research Institute of State Grid Shandong Electric Power Company,Ji’nan 250002,China;School of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《电测与仪表》
北大核心
2020年第14期106-112,共7页
Electrical Measurement & Instrumentation
关键词
智能电能表
自动化检定流水线
检定数据
表位
在线异常检测
局部异常因子
smart meter
automatic verification assembly line
verification data
meter position
online anomaly detection
local outlier factor
