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基于数据驱动的配电自动化主站系统故障检测技术

Data-Driven Fault Detection Technology for Distribution Automation Master Station System

  • 摘要: 针对配电自动化主站系统在面临大规模分布式源荷接入与复杂拓扑变更时,极易发生通信雪崩、双机同步挂起及馈线自动化(FA)服务假死等隐蔽性故障的问题,本文采集主站操作系统的多维时序运行性能数据、网络报文流量和系统总线文本日志,通过文本模板提取与时空对齐构建统一的运行状态特征矩阵。在仿真实验环境和实际配网调度中进行验证,结果表明所提技术能有效缩短隐蔽性故障定位时间。

     

    Abstract: Aiming at the hidden faults such as communication avalanche, dual-machine synchronization suspension and feeder automation (FA) service suspended animation in distribution automation master station system under large-scale distributed source-load access and complex topology changes, this paper collects multi-dimensional time-series operation performance data of the master station operating system, network message traffic and system bus text logs, and constructs a unified operation state feature matrix through text template extraction and spatiotemporal alignment. Verified in simulation environment and actual distribution network dispatching, the results show that the proposed technology can effectively shorten the location time of hidden faults.

     

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