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Partial discharge pattern recognition method based on variable predictive model-based class discriminate and partial least squares regression

机译：基于基于变量预测模型的类别判别和偏最小二乘回归的局部放电模式识别方法

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摘要

Both the feature extraction method and pattern recognition method are of great importance to assess the health condition of a power transformer. Since the partial discharge (PD) signals of the transformer are non-stationary and non-linear, and the existing pattern recognition methods fail to capitalise on the inter-relations between the extracted features of the signals, a novel pattern recognition method, namely variable predictive model based class discrimination (VPMCD) is introduced for the PD pattern recognition in this research. However, the parameters of VPMCD are estimated by using least squares (LS) regression which is sensitive to multiple correlations between independent variables. Fortunately, partial LS (PLS) regression is usable even if features are highly correlated or the number of trained samples is very small. It is novelly adopted to overcome the defects of LS regression. Therefore, an automatic PD source classifier based on PLS and VPMCD, i.e. PLS-VPMCD, is put forward in this study. PD signal features from either pulse shape characterisations or phase-resolved PD are extracted. Then, the features are used as the input vectors of PLS-VPMCD classifier. PD signals sampled from four artificial defect models are adopted for the algorithms testing. Compared with the original VPMCD and back propagation recognition methods, PLS-VPMCD has much higher recognition accuracy.
机译：特征提取方法和模式识别方法对于评估电力变压器的健康状况都非常重要。由于变压器的局部放电（PD）信号是非平稳且非线性的，并且现有的模式识别方法无法充分利用信号提取特征之间的相互关系，因此，一种新颖的模式识别方法即可变在本研究中，引入了基于预测模型的类别歧视（VPMCD）来进行PD模式识别。但是，VPMCD的参数是通过使用最小二乘（LS）回归估计的，该回归对自变量之间的多重相关敏感。幸运的是，即使特征高度相关或经过训练的样本数量很少，部分LS（PLS）回归仍然可用。它被新颖地用来克服LS回归的缺陷。因此，本研究提出了一种基于PLS和VPMCD的自动PD源分类器，即PLS-VPMCD。从脉冲形状表征或相位分辨的PD中提取PD信号特征。然后，将这些特征用作PLS-VPMCD分类器的输入向量。算法测试采用从四个人工缺陷模型采样的局部放电信号。与原始的VPMCD和反向传播识别方法相比，PLS-VPMCD具有更高的识别精度。

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