基于HALT试验的运行智能电能表剩余寿命预测方法研究
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摘要
目前,国内运行智能电能表数量庞大,约为5.5亿只。运行中智能电能表的计量准确性直接关系着电网公司和用户的切身利益。由于智能电能表运行环境千差万别,运行状态完全不一致,实际寿命不一致,因此,需要对智能电能表的实际状态进行评估,开展寿命预测研究,以便于更好地进行质量管控。本文结合环境特征对于误差的影响分析,利用聚类方法划分环境区域,在不同区域内对于运行一年的智能电能表进行抽样,建立了智能电能表Weibull剩余寿命分布模型。同时,基于HALT加速试验验证了提出的剩余寿命预测模型的可行性,该方法的提出将为制定智能电能表的轮换政策提供技术支持。
At present,the numbers of the smart meters for operation is very large,about 550 million.The accuracy of smart meters for operation is closely related to the e vital interests of grid companies and the users.Due to the difference of the operation environment of smart meters,the real life of the smart meter is a big difference,so it is need to evaluate the real status of the smart meter and predict the rest of the life.In this paper,analysing the influence of the environment on the error characteristics,dividing the regions on account of the clustering method,the smart meters for operation more than one year have been selected by stratified sampling method,and a sampling life tolerance model is established.At the same time,it is verified that the proposed life prediction model is feasibility,based on HALT accelerated test,and it will provide technical support for the formulation of the rotation policy of smart meters.
At present,the numbers of the smart meters for operation is very large,about 550 million.The accuracy of smart meters for operation is closely related to the e vital interests of grid companies and the users.Due to the difference of the operation environment of smart meters,the real life of the smart meter is a big difference,so it is need to evaluate the real status of the smart meter and predict the rest of the life.In this paper,analysing the influence of the environment on the error characteristics,dividing the regions on account of the clustering method,the smart meters for operation more than one year have been selected by stratified sampling method,and a sampling life tolerance model is established.At the same time,it is verified that the proposed life prediction model is feasibility,based on HALT accelerated test,and it will provide technical support for the formulation of the rotation policy of smart meters.
引文
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[15] 王正,王增全,谢里阳.具有“浴盆”型失效率变化规律的产品寿命概率分布模型[J].机械工程学报,2015,51(24),193-211.
[2] 袁静.论电能表运行管理[J].电测与仪表,2016,53(15A),125-127.
[3] 冯守超.单相智能电能表电气性能评价方法研究[D].北京:华北电力大学,2013.
[4] 徐人恒,曲井致,李迪星等.智能电能表全寿命周期管理系统的研究[J].电测与仪表,2017,54(1),67-70.
[5] ZHANG Penghe.Study of electric parameter test methods and failure mechanism for opticalcoupler[C].Electronics and Communication ngineering(CECE2017),2017,6,29-34.
[6] YIP Sook-Chin,WONG KokSheik,HEW Wooi-Pingw,etc.Detection of energy theft and detective smart meters in smart grids using linear regression[J].International Journal of Electrical Power and Energy Systems,2017,44(5),35-42.
[7] 薛阳,杜新纲,张蓬鹤,等.电能表故障与地域气候行业负荷关系研究[J].中国电力,2017,50(8),98-105.
[8] 罗群,刘春雨,张家安,等.智能电能表状态检验评价指标体系及在线平台开发[J].电测与仪表,2017,54(17),94-111.
[9] 依溥治,徐人恒,张明远,等.基于云平台的电能表可靠性预计系统设计与实现[J].电测与仪表,2017,54(5),96-100.
[10] 陈喜峰,王海滨,徐人恒,等.基于贝叶斯网络的智能电能表可靠性预计研究[J].电测与仪表,2017,54(23),99-104.
[11] 梁捷,李刚.电子式电能表高温摸底与步进应力试验设计[J].仪器仪表用户,2017,24(12),25-27.
[12] 陈金涛,朱彬若,张垠,等.智能电能表的加速退化试验方案研究[J].电测与仪表,2018,55(3):104-108.
[13] 朱东升,储鹏飞,李天阳,等.拆回电能表分拣架构设计与实现[J].自动化仪表,2018,39(11):6-10.
[14] 徐人恒,依溥治,王海滨,等.基于威布尔分布的电能表可靠性分析[J].自动化与仪器仪表,2017,7,56-58.
[15] 王正,王增全,谢里阳.具有“浴盆”型失效率变化规律的产品寿命概率分布模型[J].机械工程学报,2015,51(24),193-211.