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Comparison of CWT DWT based Algorithms in combination with ANNfor Protection of Power Transformer

机译:基于CWT和DWT的算法与用于保护电力变压器的算法

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This paper mainly presents comparison between intelligent algorithms based on Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) in combination with Artificial Neural Network to discriminate the magnetizing inrush current signals from the internal fault current signals of the power transformer. This work also includes development of CWT and DWT based preprocessing units to extract distinguishing attributes from inrush and internals fault signals, which are quicker, completely independent from the traditional second harmonic restraining methodologies. Extracted attributes are fed to ANN based post processingunit to classify inrush current and internal fault current of power transformer. Proposed scheme achieves proper classification with high discrimination rate and least error, avoiding mal tripping of power transformer.
机译:本文主要介绍了基于连续小波变换(CWT)和离散小波变换(DWT)的智能算法与人工神经网络的比较,以区分从电力变压器的内部故障电流信号区分磁化浪涌电流信号。这项工作还包括开发CWT和DWT的预处理单元,以提取来自涌入和内部故障信号的区分属性,这些故障信号更快,完全独立于传统的第二次谐波抑制方法。提取的属性将被馈送到基于ANN的后处理局,以对电力变压器的浪涌电流和内部故障电流进行分类。提出的方案实现了具有高鉴别率和最不误差的适当分类,避免了电力变压器的MAL跳闸。

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