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首页> 外文期刊>Electric Power Components and Systems >Detection, Classification, and Estimation of Fault Location on an Overhead Transmission Line Using S-transform and Neural Network
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Detection, Classification, and Estimation of Fault Location on an Overhead Transmission Line Using S-transform and Neural Network

机译：使用S变换和神经网络的架空输电线路故障位置的检测，分类和估计

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摘要

This article demonstrates a technique for diagnosis of fault type and faulty phase on overhead transmission lines. A method for computation of fault location is also incorporated in this work. The proposed method is based on the multi-resolution S-transform, which is used for generating complex S-matrices of the current signals measured at the sending and receiving ends of the line. The peak magnitude of the absolute value of every S'-matrix is noted. The phase angle corresponding to every peak component is obtained from the argument of the relevant S-matrix. These features are used as input vectors of a probabilistic neural network for fault detection and classification. Detection of faulty phase(s) is followed by estimation of fault location. The voltage signal of the affected phase is processed to generate the S-matrix. The frequency components of the S-matrices for different fault locations are used as input vectors for training a back-propagation neural network. The results are obtained with satisfactory accuracy and speed. All the simulations have been done in MATLAB (The Math Works, Natick, Massachusetts, USA) environment for different values of fault locations, fault resistances, and fault inception angles. The effect of noise on both the current and voltage signals has been investigated.
机译：本文演示了一种诊断架空输电线路故障类型和故障相位的技术。这项工作中还包含了一种计算故障位置的方法。所提出的方法基于多分辨率S变换，该多分辨率S变换用于生成在线路的发送和接收端处测量的电流信号的复数S矩阵。记录每个S'矩阵的绝对值的峰值。从相关S矩阵的自变量中获得与每个峰值分量相对应的相角。这些特征用作概率神经网络的输入向量，用于故障检测和分类。在检测到一个或多个故障相之后，估计故障位置。处理受影响相的电压信号以生成S矩阵。用于不同故障位置的S矩阵的频率分量用作训练反向传播神经网络的输入向量。以令人满意的精度和速度获得结果。所有仿真均已在MATLAB（美国马萨诸塞州内蒂克的数学工厂，The Math Works）环境中完成，以实现不同位置的故障位置，故障电阻和故障起始角度。已经研究了噪声对电流和电压信号的影响。

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