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Identification of Transformer Internal Faults by Using an RBF Network Based on Dynamical Principle Component Analysis

机译:通过基于动态原理分析的RBF网络识别变压器内部故障

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In this paper; a method is proposed to detect and identify parameter faults in nonlinear dynamical systems. The approach is based on the principal component analysis (PCA) and artificial neural networks (ANNs) based on radial basis functions (RBFs). A nonlinear system''s input and output data is manipulated without taking consideration any model in the approach. The method is applied to a three phase custom built transformer in order to detect and identify internal short circuit faults. It is obsered theughgh various application examples that the proposed method leads to satisfactory results in terms of detecting parameter faults in non-linear dynamical systems.
机译:在本文中;提出了一种方法来检测和识别非线性动力系统中的参数故障。该方法基于基于径向基函数(RBF)的主成分分析(PCA)和人工神经网络(ANN)。不考虑方法的任何模型,操作非线性系统的输入和输出数据。该方法应用于三相定制构建的变压器,以检测和识别内部短路故障。它被讨论了ughgh各种应用实例,即所提出的方法在非线性动力系统中检测参数故障方面导致令人满意的结果。

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