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基于深度学习的配电线路运行故障检测方法研究

Research on Fault Detection Method for Distribution Lines Based on Deep Learning

  • 摘要: 配电网安全运行面临故障类型复杂、定位精度低及传统方法效率不足等挑战。为此,本研究提出融合多源信号采集、深度特征学习与融合定位机制的智能检测方法。实验表明,该方法故障类型识别率达98.7%,定位误差小于1%,为智能电网故障精准诊断提供了新方法。

     

    Abstract: The safe operation of distribution network is faced with challenges such as complex fault types, low positioning accuracy and insufficient efficiency of traditional methods. Based on this, an intelligent detection method combining multi-source signal acquisition, deep feature learning and fusion positioning mechanism is proposed. Experiments show that the fault type recognition rate of this method breaks through 98.7%, and the positioning error is less than 1%, which provides an effective new path for accurate fault diagnosis of smart grid.

     

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