Abstract
Gas insulated switchgears (GIS) occupy an important position in the power system. Feature extraction is the key to GIS partial discharge pattern recognition, but the dimension of feature space is high, based on this, the article introduce the principal component sparse thoughts, firstly, through the 252 kV GIS partial discharge simulation experiment platform, set up the typical GIS partial discharge models, and uses AE method to obtain the signals, and through the principal component contribution rate to decide the degree of sparse, and according to the sparse component and the number of original features to decide the characteristics of related components, the results show that using this method can realize effective extraction of characteristic, and enhance the clustering results.
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