Abstract
This paper tackles the problem of the classification of partial discharge (PD) and noise signals by applying unsupervised and semi-supervised learning methods. The first step in the proposed methodology is to prepare a set of classification features from the statistical moments of the distribution of the Wavelet detail coefficients extracted from a dataset of signals acquired from a test cell under 40 kVDC. In a second step, an unsupervised learning framework that implements the k–means algorithm is applied to reduce the dimensionality of this initial feature set. The Silhouette index is used to evaluate the number of natural clusters in the dataset while the Dunn index is used to determine which subset of features produces the best clustering quality. Since the unsupervised learning does not provide any method for result validation, then the third step in the methodology of this paper consists of applying a semi-supervised learning framework that implements Transductive Support-Vector Machines. The labeling of the test set that is required in this framework for the result validation is carried out by visual checking of the signal waveforms assisted by GUI tools such as the software PDflex. The results using this methodology showed a high classification accuracy and proved that both learning frameworks can be combined to optimize the selection of classification features.
Highlights
Partial Discharge (PD) phenomena and measurements have become a vital technique to assess the condition of the insulation of HighVoltage (HV) power apparatus and cables [1,2]
In this work, unsupervised as well as semi-supervised classification methods were applied to the classification of partial discharge (PD) and noise signals collected from a test cell under 40 kVDC
A set of 20 numerical features formed by the mean, variance, skewness and kurtosis of the wavelet detail coefficient distribution at each level of decomposition were extracted from each acquired signal
Summary
Partial Discharge (PD) phenomena and measurements have become a vital technique to assess the condition of the insulation of HighVoltage (HV) power apparatus and cables [1,2]. Spectral power ratios analysis, time frequency maps, among many other more, have been applied for the extraction of features, that combined with different unsupervised clustering algorithms have shown good results for PD and noise signals separation in multiple experimental setups. An advantage of semi-supervised learning is that a test set can be built from labeled data to evaluate the classification performance of the algorithm This contributes to reduce the complexity of clustering results validation in unsupervised learning and in turn, the validation can be performed automatically, without the need of visual verification from an expert. Semi-supervised learning has recently become popular due to the variety of cases where a lot of unlabeled data are available, for example text classification [12] or image processing [13] This field has not been fully investigated for Partial Discharge monitoring and especially for PD-noise pattern classification. We discussed the high classification performance achieved by labeling a small share of the dataset
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