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Fault classification on transmission line of 10kV rural power grid

机译:10KV农村电网输电线路故障分类

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This paper proposes a technique using Discrete Wavelet Transform (DWT) and Back-Propagation Neural Network (BPNN) to identify the fault types on transmission line of 10kv rural power grid. The PSCAD is used to simulate fault signals. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The variations of first scale high frequency component that detect fault are used as an input for the training pattern. The result has shown that the proposed technique gives satisfactory results.
机译:本文提出了一种使用离散小波变换(DWT)和背传播神经网络(BPNN)的技术,以识别10kV农村电网传输线上的故障类型。 PSCAD用于模拟故障信号。母小波Daubechies4(DB4)用于与这些信号分解高频分量。检测故障的第一刻度高频分量的变型用作训练模式的输入。结果表明,该技术提供了令人满意的结果。

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