Abstract
Accurately identifying the types of insulation defects inside a gas-insulated switchgear (GIS) is of great significance for guiding maintenance work as well as ensuring the safe and stable operation of GIS. By building a set of 220 kV high-voltage direct current (HVDC) GIS experiment platforms and manufacturing four different types of insulation defects (including multiple sizes and positions), 180,828 pulse current signals under multiple voltage levels are successfully measured. Then, the apparent discharge quantity and the discharge time, two inherent physical quantities unaffected by the experimental platform and measurement system, are obtained after the pulse current signal is denoised, according to which 70 statistical features are extracted. In this paper, a pattern recognition method based on generalized discriminant component analysis driven support vector machine (SVM) is detailed and the corresponding selection criterion of involved parameters is established. The results show that the newly proposed pattern recognition method greatly improves the recognition accuracy of fault diagnosis in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms and 44 kinds of state-of-the-art classifiers. This newly proposed method not only solves the difficulty that phase-resolved partial discharge (PRPD) cannot be applied under DC condition but also immensely facilitates the fault diagnosis of HVDC GIS.
Highlights
In order to demonstrate the advantages of the newly proposed pattern recognition method based on generalized discriminant component analysis (GDCA) and its kernelization forms driven support vector machine (SVM), the test strategy of recognition effect based on the combination of Monte-Carlo experimental method and cross-validation is put forward firstly by which a wealth of estimation indicators for classification results can be calculated
The criterion aimed at finding the optimal (α, δ) value-pair for GDCA and the optimal (γ, α, δ) value-pair for GDCA’s kernelization forms is given, through which it is possible to optimally select the parameters involved in GDCA and its kernelization forms in advance without using the estimation indicators for classification results, greatly shortening the time of pattern recognition and ensuring the optimal recognition effect
Random sampling is performed on the uniform distribution, so that the recognition vectors of all the discharge sample points are divided into five disjoint folds, and 5-fold cross-validation is performed
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The results show that the newly proposed pattern recognition method greatly improves the recognition accuracy in comparison with 36 kinds of state-of-theart dimensionality reduction algorithms and 44 kinds of state-of-the-art classifiers This newly proposed method solves the difficulty that phase-resolved partial discharge (PRPD) cannot be applied under DC conditions and immensely facilitates the fault diagnosis of HVDC GIS. The subsequent structure of this paper is arranged as follows: Section 2 introduces the GIS experimental platform and insulation defect settings; Section 3 describes the 70 statistical features extracted from thepaper inherent physicalasquantities of pulse current signal; The subsequent structure of this is arranged follows: Section introduces the Section. 5 gives extracted the resultsfrom andthe discussions of the newly proposed pattern recognition method cal features inherent physical quantities of pulse current signal; Section based on the GDCA and and its kernelization and the paper concluded in proposes theories algorithms offorms.
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