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首页> 外文期刊>Electric Power Components and Systems >Feed-forward Artificial Neural Network-Discrete Wavelet Transform Approach to Classify Power System Transients
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Feed-forward Artificial Neural Network-Discrete Wavelet Transform Approach to Classify Power System Transients

机译：电力系统暂态分类的前馈人工神经网络离散小波变换方法

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摘要

Switching transients in power systems are always a concern in studies of equipment insulation coordination. However, with widespread use of sensitive non-linear electronic devices, these transients are capable of degrading the quality of power. Utilities often switch the shunt capacitor banks to cope with sagging voltage levels, thereby generating transients that travel into the network of end users.Capacitor switching can cause over-voltage, resonance, and inadvertent tripping of adjustable speed drives and many other sensitive electronic devices. This article presents a method to distinguish between transients arising out of isolated capacitor switching, back-to-back capacitor switching, load switching, various line faults, and line switching. Discrete wavelet transform of the modal voltage signal is used to extract distinguishing features from the voltage waveform of these events. The detailed coefficients for dl and d5 level only, obtained from discrete wavelet transform, are processed, mapped, and given to the feed-forward artificial neural network, which classifies the event accordingly. A real power system has been simulated in PSCAD/EMTDC with lines modeled using a frequency-dependent phase model, and its results are then fed to MATLAB (The MathWorks, Natick, Massachusetts, USA) for implementation of the scheme using the feed-forward artificial neural network.
机译：电力系统中的开关瞬变始终是设备绝缘协调研究中的一个关注点。然而，随着灵敏非线性电子设备的广泛使用，这些瞬变能够降低电源质量。公用事业公司通常会切换并联电容器组以应对不断下降的电压电平，从而产生瞬态信号，这些瞬态信号会传播到最终用户的网络中。电容器切换会导致过电压，谐振以及可调速驱动器和许多其他敏感电子设备的意外跳闸。本文提出了一种方法来区分由隔离电容器切换，背对背电容器切换，负载切换，各种线路故障和线路切换引起的瞬变。模态电压信号的离散小波变换用于从这些事件的电压波形中提取出明显的特征。仅对从离散小波变换获得的dl和d5级别的详细系数进行处理，映射，并提供给前馈人工神经网络，该网络将事件进行相应的分类。已在PSCAD / EMTDC中模拟了一个真实的电源系统，并使用了一个与频率相关的相位模型对线路进行建模，然后将其结果馈送到MATLAB（美国马萨诸塞州纳蒂克的MathWorks公司），以使用前馈方法实施该方案。人工神经网络。

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