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Critical investigations on performance of ANN and wavelet fault classifiers

机译:人工神经网络和小波故障分类器性能的关键研究

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With increasing demands and competitive business environment, the structure of power system has become very complex. Moreover, power system is a dynamic framework due to faults and rapid load variations. Hence, the detection algorithms for faults are potential areas of research. To discuss this issue and to provide the solution methodology for detection of faults and further classification of those in a smart grid is a primary motivation of this manuscript. This paper presents application of supervised learning algorithms based on different neural network topologies for detection and classification of the faults in transmission lines in power system. Different wavelet transforms on different Multi Resolution Analysis levels are applied for detection of the potential features from the voltage waveforms of the Phasor Measurement Units (PMUs). These wavelet transforms are then applied to several neural networks classification engines to classify faults. Binary classification technique is used for definitions of faults. Different faults namely single line to ground, line to line, double line to ground and three phase symmetrical faults are designated as a binary digit. These definitions are employed to train the classification engine. Different plots of confusion and errors are plotted to establish a fair comparison between supervised learning algorithms.
机译:随着需求的增加和竞争激烈的商业环境,电力系统的结构变得非常复杂。此外,由于故障和负载快速变化,电力系统是一个动态框架。因此,故障检测算法是潜在的研究领域。讨论此问题并提供解决方法以检测故障并进一步对智能电网中的故障进行分类是该手稿的主要动机。本文提出了基于不同神经网络拓扑的监督学习算法在电力系统传输线故障检测和分类中的应用。将不同多分辨率分析级别上的不同小波变换应用于相量测量单元(PMU)的电压波形中的潜在特征检测。然后将这些小波变换应用于几个神经网络分类引擎以对故障进行分类。二进制分类技术用于故障定义。将不同的故障(即单线接地,线对线,双线接地和三相对称故障)指定为二进制数字。这些定义用于训练分类引擎。绘制了不同的混乱和错误图,以建立监督学习算法之间的公平比较。

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