2025年4月6日 周日
首页 > 过刊浏览>2023年第38卷第1期 >218-225. DOI:10.19781/j.issn.1673-9140.2023.01.025
PDF HTML阅读 XML下载 导出引用 引用提醒
基于小样本故障率的智能电能表可靠度预估模型
CSTR:
[cstr]
作者:
作者单位:

(1.国网山东省电力公司营销服务中心(计量中心)，山东 济南 250000;2.国网山东省电力公司电力科学研究院，山东 济南 250000)

通讯作者:

代燕杰（1983—），女，硕士，高级工程师，主要从事电力营销研究;E?mail：daiyanjie@sd.sgcc.com.cn

中图分类号:

TM933.4

基金项目:

国网山东省电力公司科技项目（520626200021）


Reliability prediction model based on small sample failure rate of smart meter
Author:
Affiliation:

(1.State Grid Shandong Electric Power Company Marketing Service Center (Metering Center), Jinan 250000, China;2.Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250000, China)

  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [17]
    • | | | |
  • 文章评论
    摘要:

    基于故障率数据的可靠度评估是智能电能表健康状态管理与维修的重要依据。然而,异常值及故障率的小样本特性限制了传统智能电能表可靠度预测模型的评估性能。为此，提出一种基于加权局部离群因子与高斯过程回归的多环境应力下智能电能表故障率预估模型。首先，建模采用一种加权局部离群因子识别并剔除故障率数据集中的异常值；然后，选用不同的核函数匹配典型环境下的多应力输入特征，选取最优核；最后，以高斯过程后验分布预测故障率的置信度95%的区间变化，并据此估计智能电能表可靠度。采用2个典型环境地区的智能电能表故障样本进行实例分析，结果表明所提模型可有效预测智能电能表在多环境应力下故障率变化趋势，并能准确求解其可靠度。

    Abstract:

    Reliability evaluation based on the failure rate data is an important basis for the health status management and maintenance of smart meters. However, the small sample characteristics of outliers and failure rates limit the evaluation performance of traditional smart energy meter reliability prediction models. Therefore, a prediction model of smart meter failure rate under multi?environment stress based on weighted local outlier factor and Gaussian process regression is proposed in this paper. Firstly, a weighted local outlier factor is employed with the model to identify and then delete potential outliers in failure rate data sets; then, different kernel functions are selected to match the characteristics of multiple stress inputs in typical environments, and choose the best one. Finally, the interval change of the 95% confidence level of the failure rate is predicted by the posterior distribution of the Gaussian process, and the interval reliability is obtained based on this. Case analysis of fault samples of smart meters in two typical environmental areas shows that the proposed model could effectively predict the trend of failure rate of smart meters under multi?environmental stress, and could accurately solve its reliability.

    参考文献
    [1] 杜蜀薇,成达,邱伟,等.基于分层贝叶斯的电能表短期故障评估与预测[J].电子测量与仪器学报,2018,32(12): 178?184. DU Shuwei,CHENG Da,QIU Wei,et al.Short?term fault evaluation and prediction of energy meterbased on hierarchical Bayesian[J].Journal of Electronic Measurement and Instrument,2018,32(12):178?184.
    [2] 宋晓川，郑宽昀，李俊臣，等. 基于计量自动化系统的电能计量异常诊断技术研究[J]. 电网与清洁能源，2022，38(4)：110?116. SONG Xiaochuan，ZHENG Kuanyun，LI Junchen，et al. Research on abnormal diagnosis technology of electric energy metering based on metering automation system[J].Power System and Clean Energy，2022，38(4)：110?116.
    [3] 刘永光,谭赣江,刘型志,等.基于域自适应的二屏电能表显示屏质量检测方法[J].电力系统保护与控制,2022,50(23):180?187. LIU Yongguang,TAN Ganjiang,LIU Xingzhi,et al.Display quality detection method for a two?screen watt?hour meter based on domain adaptation[J].Power System Protection and Control,2022,50(23):180?187.
    [4] QIU W,TANG Q,YAO W X,et al.Probability analysis for failure assessment of electric energy metering equipments under multiple extreme stresses[J].IEEE Transactions on Industrial Informatics,2021,17(6):3762?3771.
    [5] 詹坤,高广德,李建忠,等.基于定频PWM稳压的电流互感器取能电源设计方法[J].智慧电力,2021,49(12):45?51. ZHAN Kun,GAO Guangde,LI Jianzhong,et al.Design method of current transformer energy extracting power supply based on constant frequency PWM voltage stabilization[J].Smart Power,2021,49(12):45?51.
    [6] 曹宏宇,刘惠颖,殷鑫,等.典型环境下智能电能表可靠性指标体系及指标量化[J].电测与仪表,2021,58(3):190?194. CAO Hongyu,LIU Huiying,YIN Xin, et al.Reliability index system and quantification of smart meter in typical environment[J].Electrical Measurement & Instrumentation,2021,58(3):190?194.
    [7] MO Y C.A multiple?valued decision?diagram?based approach to solve dynamic fault trees[J].IEEE Transactions on Reliability,2014,63(1):81?93.
    [8] 孙谊媊,李宁,董小顺,等.智能电表可靠性预计的优化模型[J].电力科学与技术学报,2017,32(3):15?21. SUN Yisheng,LI Ning,DONG Xiaoshun,et al.Optimization model for reliability prediction of smart meters[J].Journal of Electric Power Science and Technology,2017,32(3):15?21.
    [9] 彭宇,刘大同.数据驱动故障预测和健康管理综述[J].仪器仪表学报,2014,35(3):481?495. PENG Yu,LIU Datong.Data?driven prognostics and health management:a review of recent advances[J].Chinese Journal of Scientific Instrument,2014,35(3):481?495.
    [10] 胡昌华,施权,司小胜,等.数据驱动的寿命预测和健康管理技术研究进展[J].信息与控制,2017,46(1):72?82. HU Changhua,SHI Quan,SI Xiaosheng,et al.Data?driven life prediction and health management technology research progress[J].Information and Control,2017,46(1):72?82.
    [11] 张鼎衢,杨路,宋强,等.一种用于关口电能计量装置远程校验的多路模拟采集器的研制[J].电测与仪表,2022, 59(9):181?187. ZHANG Dingqu,YANG Lu,SONG Qiang,et al.Development of a multi?channel analog collector for remote calibration of gatewayelectric energy metering device[J].Electrical Measurement & Instrumentation, 2022,59(9):181?187.
    [12] ALI J B,CHEBEL?MORELLO B,SAIDI L,et al.Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network[J].Mechanical Systems and Signal Processing,2015,56(5):150?172.
    [13] 刘影,刘岩,燕凯，等.基于智能电能表的智慧城市峰值负荷概率估计[J].电测与仪表,2021,58(9):166?171. LIU Ying,LIU Yan,YAN Kai,et al.Peak load probability estimation of smart city based on smart meter[J].Electrical Measurement & Instrumentation,2021,58(9):166?171.
    [14] SHENG H M,XIAO J,CHENG Y H,et al.Short?term solar power forecasting based on weighted gaussian process regression[J].IEEE Transactions on Industrial Electronics,2018,65(1):300?308.
    [15] 陈叶,韩彤,魏龄,等.基于多分类融合模型的智能电能表故障预测[J].电测与仪表,2022,59(11):162?168. CHEN Ye,HAN Tong,WEI Ling,et al.Prediction on fault classification of smart meters based on multi? classification integration model[J].Electrical Measurement & Instrumentation,2022,59(11):162?168.
    [16] 黄友朋,路韬,陈亮,等.智能电能表批次故障预警和寿命预估方法[J].中国电力,2022,55(7):87?92. HUANG Youpeng,LU Tao,CHEN Liang,et al.Failure early warning and life estimation method of batch smart meter[J].Electric Power,2022,55(7):87-92.
    [17] RASMUSSEN C E.Gaussian processes for machine learning[M].Cambridge:MIT Press,2006,56?61.
    相似文献
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

陈祉如,代燕杰,杜 艳,等.基于小样本故障率的智能电能表可靠度预估模型[J].电力科学与技术学报,2023,38(1):218-225.
CHEN Zhiru, DAI Yanjie, DU Yan, et al. Reliability prediction model based on small sample failure rate of smart meter[J]. Journal of Electric Power Science and Technology,2023,38(1):218-225.

复制
分享
文章指标
  • 点击次数:253
  • 下载次数: 1106
  • HTML阅读次数: 0
  • 引用次数: 0
历史
  • 在线发布日期: 2023-04-10
文章二维码

微信公众号二维码

您是本站第 84076 访问者

通信地址：长沙市（天心区）万家丽南路960号长沙理工大学

电话/传真：0731-85258195 E-mail：dlxb04@163.com

版权所有：电力科学与技术学报 ® 2025 版权所有

京公网安备 11010802026262号