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CN 32-1180/TP

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基于信息融合的低压智能电能表动态评价模型
作者:
作者单位:

1.中国计量大学机电工程学院,浙江省 杭州市 310018;2.国网浙江省电力有限公司电力科学研究院,浙江省 杭州市 310014;3.浙江华云信息科技有限公司,浙江省 杭州市 310000

作者简介:

通讯作者:

基金项目:

浙江省公益技术研究计划/工业项目(LGG18E070004)。


Information Fusion Based Dynamic Evaluation Model of Low-voltage Intelligent Electric Energy Meter
Author:
Affiliation:

1.College of Mechanical and Electrical Engineering,China Jiliang University, Hangzhou 310018, China;2.Electric Power Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310014, China;3.Zhejiang Huayun Information Technology Co., Ltd., Hangzhou 310000, China

Fund Project:

This work is supported by Zhejiang Provincial Public Welfare Foundation of China (No. LGG18E070004).

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    摘要:

    针对智能电能表状态评价技术有待完善的问题,基于信息融合理论建立了一种新的低压智能电能表动态评价模型。该模型对低压智能电能表可靠度进行分析的同时,综合考虑计量异常、全事件、电能表过载率、时钟电池异常4种状态因素,采用熵值法实时计算各项指标权值,并且结合地区影响因素,对低压智能电能表进行动态状态评价。基于威布尔分布理论构建电能表可靠度子评价模型,应用贝叶斯公式建立计量异常和全事件子评价模型,引入泰尔指数评价地区影响因素。采用实际运行数据对该评价模型进行验证,结果表明所提模型合理、可行,能够对智能电表进行有效评价。

    Abstract:

    Aiming at the problem that the state evaluation technology of intelligent electricity meter needs to be perfected, this paper establishes a new dynamic evaluation model of low-voltage intelligent electricity meter based on information fusion theory. While analyzing the reliability of the low-voltage intelligent electricity meter, the four state factors, namely abnormal measurement, full event, overload rate of the electricity meter and abnormal clock battery are comprehensively considered, and the entropy method is adopted to calculate the weight of each index in real-time. In addition, the dynamic state evaluation of the low-voltage intelligent electricity meter is made based on the regional influence factors. Based on Weibull distribution theory, a sub-evaluation model of reliability for electricity meter is developed, a sub-evaluation model of measurement anomaly and full event is established by using Bayesian formula, and the Thiel index is introduced to evaluate area influential factors. The evaluation model is verified by the actual operation data, and the results show that the developed model is reasonable and feasible, and can effectively evaluate the intelligent electricity meter.

    表 3 6月所有电能表评价结果Table 3 Evaluation results of all electrical energy meters in June
    表 2 第1类全事件故障概率Table 2 Fault probability of the first kind of total event
    表 1 第1类计量异常事件故障概率Table 1 Failure probability of the first kind of measurement abnormal event
    图1 6月所有表概率密度分布图Fig.1 Probability density distribution map of all electric energy meters in June
    图2 6月故障电能表概率密度分布图Fig.2 Probability density distribution map of fault electric energy meters in June
    图 6月和8月故障电能表分布情况对比Fig. Distribution comparison of faulty electric energy meters in June and August
    表 5 6月和8月经过检定的拆回电能表评价指标结果Table 5 Evaluation index results of removed electric energy meters in June and August
    表 6 Table 6
    表 7 Table 7
    表 4 6月拆回电能表评价结果Table 4 Evaluation results of all electrical energy meters in June
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引用本文

蔡慧,乔适苏,袁健,等.基于信息融合的低压智能电能表动态评价模型[J/OL].电力系统自动化,http://doi.org/10.7500/AEPS20190528006.
CAI Hui,QIAO Shisu,YUAN Jian,et al.Information Fusion Based Dynamic Evaluation Model of Low-voltage Intelligent Electric Energy Meter[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20190528006.

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  • 收稿日期:2019-05-28
  • 最后修改日期:2020-03-12
  • 录用日期:2019-11-27
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