基于大数据的输电线路故障预警模型设计  

Design of transmission line fault early warning model based on big data

作　　者：方玉河 陶汉涛[1,2,3] 张磊 王钊 姜志博[1,2,3] 吴大伟 Fang Yuhe;Tao Hantao;Zhang Lei;Wang Zhao;Jiang Zhibo;Wu Dawei(NARI Group Corporation Ltd.,Jiangsu Nanjing,211106,China;Wuhan NARI Limited Liability Company,State Grid Electric Power Research Institute,Hubei Wuhan,430074,China;Hubei Key Laboratory of Power Grid Lightning Risk Prevention,Hubei Wuhan,430074,China)

机构地区：[1]南瑞集团有限公司,江苏南京211106 [2]国网电力科学研究院武汉南瑞有限责任公司,湖北武汉430074 [3]电网雷击风险预防湖北省重点实验室,湖北武汉430074

出　　处：《机械设计与制造工程》2021年第10期110-114,共5页Machine Design and Manufacturing Engineering

摘　　要：针对当前输电线路故障预警存在精度低的难题,设计基于大数据的输电线路故障预警模型。首先采集故障相关的特征数据,利用粗神经网络算法对数据内故障因子进行挖掘,然后利用朴素贝叶斯算法计算故障因子发生指数,结合时间序列一致性的故障匹配算法完成输电线路故障预警。测试结果表明,该模型能够有效且全面地挖掘故障因子,准确预警输电线路故障。Aiming at the problem of low accuracy of transmission line fault early warning,a transmission line fault early warning model based on big data is designed.Firstly,the fault related characteristic data are collected from multiple directions,and the fault factors in the data are mined by using the learning of rough neural network algorithm.Then,the occurrence index of fault factors is calculated by using naive Bayesian algorithm,and the fault early warning of transmission line is completed by combining the fault matching algorithm of time series consistency.Finally,the test results show that this model can effectively and comprehensively mine fault factors and accurately warn transmission line faults.

关 键 词：大数据 输电线路 故障预警 数据挖掘 粗神经网络 

分 类 号：TM75[电气工程—电力系统及自动化]