摘要
现有的变压器故障推演系统推演误差大、故障诊断时间长,为此,基于大数据关联挖掘技术设计了一种新的变压器故障推演系统。利用防火墙、特殊交换机、信息数据库组建系统硬件环境,然后通过关联分析、故障诊断、聚类推演实现软件推演程序的设计。实验结果表明,与传统系统相比,基于大数据关联挖掘的变压器故障推演系统能够更有效地降低推演误差,缩短诊断时间。
The existing transformer fault inference system has large error and long fault diagnosis time.Therefore,this study designed a new transformer fault inference system based on big data association mining technology.Firewall,special switch and information database are used to set up the system hardware environment,and then the software programming is designed through correlation analysis,fault diagnosis and cluster deduction.The experimental results show that compared with the traditional system,the transformer fault deduction system based on big data association mining can reduce the deduction error and shorten the diagnosis time more effectively.
作者
高伟
吕艳霞
阚东微
郝艳军
GAO Wei;LV Yanxia;KAN Dongwei;HAO Yanjun(State Grid Hegang Power Supply Company,Hegang 154100,China;Beijing Zhongdian Nari Technology Co.,Ltd.,Beijing 100160,China)
出处
《电子设计工程》
2021年第14期61-64,69,共5页
Electronic Design Engineering
基金
黑龙江省自然科学基金项目(1707075ME20)。
关键词
大数据
关联挖掘
变压器故障
推演系统
big data
correlation mining
transformer fault
inference system
