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Fault Classification and Localization in Power Systems Using Fault Signatures and Principal Components Analysis

机译:使用故障特征和主成分分析的电力系统故障分类与定位

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A vital attribute of electrical power network is the continuity of service with a high level of reliability. This motivated many researchers to investigate power systems in an effort to improve reliability by focusing on fault detection, classification and localization. In this paper, a new protective relaying framework to detect, classify and localize faults in an electrical power transmission system is presented. This work will extract phase current values during ( )th of a cycle to generate unique signatures. By utilizing principal component analysis (PCA) methods, this system will identify and classify any fault instantaneously. Also, by using the curve fitting polynomial technique with our index pattern obtained from the unique fault signature, the location of the fault can be determined with a significant accuracy.
机译:电力网络的重要属性是具有高度可靠性的服务连续性。这激发了许多研究人员研究电力系统,以通过专注于故障检测,分类和定位来提高可靠性。本文提出了一种新的保护继电器框架,用于检测,分类和定位电力传输系统中的故障。这项工作将在一个周期的()内提取相电流值,以生成唯一的签名。通过使用主成分分析(PCA)方法,该系统将立即识别并分类任何故障。而且,通过将曲线拟合多项式技术与我们从独特故障特征获得的索引模式一起使用,可以以很高的精度确定故障位置。

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