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首页> 外文期刊>Science, Measurement & Technology, IET >Accurate power transformer PD pattern recognition via its model
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Accurate power transformer PD pattern recognition via its model

机译：通过其模型进行准确的电力变压器PD模式识别

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摘要

In this study, a transformer model is proposed to simulate the behaviour of a real transformer, under presence of different types of defects which contribute to partial discharge (PD) generation, as closely as possible. Five different types of defects (scratch on winding insulation, bubble in oil, moisture in insulation paper, very small free metal particle in transformer tank and fixed sharp metal point on transformer tank) are implemented artificially into these transformer models to investigate the resultant PD current signal magnitude and characteristics. Time-domain PD current waveforms are recorded on those transformer models which have one type of those defects. The resultant statistical PD current wave shapes and texture features are extracted from these captured PD current signals. The principal component analysis (PCA) is used to reduce the dimension of feature spaces which are required to develop the inputs for the classifier. The principal components obtained through PCA are applied to the support vector machine classifier, as an input. The classification results indicate that the extracted texture features (using grey-level covariance matrix) preserve the best characteristics for separation of the related patterns of those five defect models, accurately.
机译：在这项研究中，提出了一种变压器模型来模拟真实变压器的行为，该模型应尽可能接近地在导致局部放电（PD）产生的不同类型缺陷的情况下进行。在这些变压器模型中人为地实现了五种不同类型的缺陷（绕组绝缘刮痕，油中的气泡，绝缘纸中的水分，变压器箱中非常小的自由金属颗粒以及变压器箱上固定的尖锐金属点），以研究产生的局部放电电流信号幅度和特性。时域PD电流波形记录在具有其中一种缺陷的那些变压器模型上。从这些捕获的PD电流信号中提取所得的统计PD电流波形和纹理特征。主成分分析（PCA）用于减少特征空间的维数，这些特征空间是开发分类器输入所需的。通过PCA获得的主要成分将作为输入应用于支持向量机分类器。分类结果表明，提取的纹理特征（使用灰度协方差矩阵）保留了用于正确分离这五个缺陷模型的相关图案的最佳特征。

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