兰州理工大学学报

兰州理工大学学报 ›› 2023, Vol. 49 ›› Issue (2): 83-87.

• 自动化技术与计算机技术 • 上一篇    下一篇

继电保护设备剩余寿命预测的智能算法研究

谢楠*, 马振国, 唐兵, 黄煜铭, 张柯琪, 曹丹怡   

  1. 国网江苏省电力有限公司常州供电分公司, 江苏 常州 213003
  • 收稿日期:2021-09-08 出版日期:2023-04-28 发布日期:2023-05-05
  • 通讯作者: 谢 楠(1972-),男,江苏武进人,工程师. Email:13601500317@sina.cn

Study on intelligent algorithm for remaining useful life prediction of relay protection equipment

XIE Nan, MA Zhen-guo, TANG Bing, HUANG Yu-ming, ZHANG Ke-qi, CAO Dan-yi   

  1. State Grid Changzhou Power Supply Company, Changzhou 213003, China
  • Received:2021-09-08 Online:2023-04-28 Published:2023-05-05

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摘要： 阐述了剩余寿命预测不仅可以提高电力系统继电保护设备检修效率,同时能够丰富国网电力设备的全生命周期管理.针对JC市供电公司的继电保护设备及其数据特征,根据属性相关的变量安装日期、损坏日期、当前日期和设计寿命,定义设计生命历程和实际生命历程.利用支持向量回归、回归树和随机森林三种方法对无监测设备的剩余寿命进行预测.案例分析表明随机森林的预测方法效果最佳.

关键词: 支持向量回归, 回归树, 随机森林, 剩余寿命, 继电保护

Abstract: Remaining useful life prediction can not only improve the maintenance efficiency of relay protection equipment of the power system but also enrich the full-lifecycle management of power equipment in the state grid. Based on the relay protection equipment of JC city power supply company and its characteristics of data, this paper defines the design life course and actual life course according to the variable installation date, damage date, current date, and design life related to attributes. Support vector regression(SVR), regression tree(RT), and random forest(RF) methods are used to predict the remaining useful life of unmonitored equipment. Case analysis shows that the prediction method of random forest performs best.

Key words: support vector regression, regression tree, random forest, remaining useful life, relay protection

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