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首页> 外文期刊>Simulation modelling practice and theory: International journal of the Federation of European Simulation Societies >Power transformer differential protection using neural network Principal Component Analysis and Radial Basis Function Neural Network
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Power transformer differential protection using neural network Principal Component Analysis and Radial Basis Function Neural Network

机译：基于神经网络主成分分析和径向基函数神经网络的电力变压器差动保护

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摘要

Many methods have been used to discriminate magnetizing inrush from internal faults in power transformers. Most of them follow a deterministic approach, i.e. they rely on an index and fixed threshold. This article proposes two approaches (i.e. NNPCA and RBFNN) for power transformer differential protection and address the challenging task of detecting magnetizing inrush from internal fault. These approaches based on the pattern recognition technique. In the proposed algorithm, the Neural Network Principal Component Analysis (NNPCA) and Radial Basis Function Neural Network (RBFNN) are used as a classifier. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and over-excitation condition. The presented algorithm also makes use of ratio of voltage-to-frequency and amplitude of differential current for detection transformer operating condition. For both proposed cases, optimal number of neurons has been considered in the neural network architectures and the effect of hidden layer neurons on the classification accuracy is analyzed. A comparison among the performance of the FFBPNN (Feed Forward Back Propagation Neural Network), NNPCA, RBFNN based classifiers and with the conventional harmonic restraint method based on Discrete Fourier Transform (DFT) method is presented in distinguishing between magnetizing inrush and internal fault condition of power transformer. The algorithm is evaluated using simulation performed with PSCAD/EMTDC and MATLAB. The results confirm that the RBFNN is faster, stable and more reliable recognition of transformer inrush and internal fault condition.
机译：已经使用了许多方法来将励磁涌流与电力变压器的内部故障区分开。它们中的大多数遵循确定性方法，即它们依赖于索引和固定阈值。本文提出了两种用于电力变压器差动保护的方法（即NNPCA和RBFNN），并解决了检测内部故障引起的励磁涌入的艰巨任务。这些方法基于模式识别技术。在所提出的算法中，神经网络主成分分析（NNPCA）和径向基函数神经网络（RBFNN）被用作分类器。主成分分析用于预处理来自电力系统的数据，以消除冗余信息并增强差动电流的隐藏模式，以区分涌入和过励磁情况引起的内部故障。该算法还利用电压频率比和差动电流幅值来检测变压器的工作状态。对于这两种提出的情况，已经在神经网络体系结构中考虑了最佳的神经元数量，并分析了隐层神经元对分类准确性的影响。提出了FFBPNN，基于NNPCA，RBFNN的分类器与传统的基于离散傅里叶变换（DFT）的谐波抑制方法在区分励磁涌流和内部故障状况方面的性能比较。电源变压器。使用PSCAD / EMTDC和MATLAB进行的仿真对算法进行了评估。结果表明，RBFNN能够更快，更稳定，更可靠地识别变压器浪涌和内部故障状况。

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