基于局部全局一致性学习算法的故障选线方法

Fault line selection method based on local global consistency learning algorithm

  • 摘要: 针对现有故障选线方法用于中性点经消弧线圈接地系统或相电压过零点附近发生故障时选线不准确的问题,提出一种基于局部全局一致性学习算法的小电流选线方法,即首先对线路接地故障原始信号进行傅里叶变换,然后将各故障信号的特征量输入局部全局一致性学习算法,通过标签循环传递判断故障特征信号,从而选出故障线路。通过Matlab仿真模型与实验室测试平台对该方法进行了研究,结果表明该方法具有较高的选线可靠性与准确性。

     

    Abstract: For inaccurate line selection problem of existing fault line selection methods used in power system connected to earth through an arc suppression coil or fault selection for phase voltage zero-crossing point, a kind of small current line selection method was proposed which was based on local global consistency learning algorithm. In the method, primary grounding fault signals are processed by Fourier transform firstly, then characteristic value of each fault signal is processed by local global consistency learning algorithm, and fault characteristic signals are judged through cyclical transmitting labels at last, so as to select fault line. The Matlab simulation and laboratory testing results verify that the method has higher reliability and accuracy of fault line selection.

     

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