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文章摘要
基于重要参量和参数修正的配电网设备故障率计算方法
Calculation method of equipment failure rate in distribution network based on important parameters and parameter correction
Received:August 14, 2019  Revised:August 14, 2019
DOI:10.19753/j.issn1001-1390.2022.02.020
中文关键词: 渐变性因素  突发性因素  实时故障率  配电网  参数修正
英文关键词: gradual factor, sudden factor, real-time failure rate, distribution network, parameter correction
基金项目:中国南方电网有限责任公司科技项目“配电网重复多发性停电防治能力提升关键技术研究及应用”(GZJKJXM20170012)
Author NameAffiliationE-mail
Lin Xiang* Guangzhou Power Supply Bureau Co.,Ltd.,Guangzhou cqulx1991@163.com 
Fang Jian Guangzhou Power Supply Bureau Co.,Ltd.,Guangzhou fjenglish@163.com 
Wang Hongbin Guangzhou Power Supply Bureau Co.,Ltd.,Guangzhou 13503095341@139.com 
Zhang Hang Guangzhou Power Supply Bureau Co.,Ltd.,Guangzhou ncepuzhanghang@126.com 
Fang Min Guangzhou Power Supply Bureau Co.,Ltd.,Guangzhou fjenglish@163.com 
Li Shengnan Guangzhou Power Supply Bureau Co.,Ltd.,Guangzhou 489685548@qq.com 
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中文摘要:
      针对现有研究中,利用配电设备的平均故障率进行建模修正的方法忽略了一些因素的影响,以及基于健康指数计算设备故障率的模型研究还不准确等问题,提出了基于重要参量输入和故障率参数修正相结合的方法。将影响配电设备故障率的因素分成渐变性因素和突发性因素;筛选出各故障模式对应的重要监测参量,采用基于重要参量输入的方法计算渐变性因素联合作用下的故障率;使用故障率参数修正模型逐一计算主要突发性因素对故障率的影响;通过实例验证了方法的有效性。该方法结合在线监测实时数据和环境的影响计算得到的设备故障率,能够准确反映同一类型设备不同个体之间的差异化实时状态。
英文摘要:
      In view of the existing research, the method of modeling and correction using the average failure rate of distribution equipment ignores the influence of some factors, and the model research based on health index to calculate the failure rate of distribution equipment is not accurate, a method based on important parameter input and fault rate parameter correction is proposed in this paper. Firstly, the factors affecting the failure rate of distribution equipment are divided into gradual factors and sudden factors. Then, the important monitoring parameters corresponding to each fault mode are selected and the failure rate under the combined action of gradual factors is calculated by using the method based on the input of important parameters. Then, the failure rate parameter correction model is adopted to calculate the impact of major sudden factors on the failure rate one by one. Finally, the effectiveness of the proposed method is verified by an example. This method can accurately reflect the differential real-time state of different individuals of the same type of equipment by combining the real-time data of online monitoring and the influence of environment to calculate the failure rate of device.
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