Partial discharge pattern recognition based on optimal uncorrelated discriminant vectors in GIS

While partial discharge (PD) pattern recognition is widely considered as an important method to evaluate insulation state, the major problem encountered in recognition is to extract PD pattern features. This paper designed four typical physical models of defects in GIS, and PD three‐dimensional patterns were constructed based on mass sample gathered by the very high frequency (VHF) and high‐speeds systems. A principal component analysis (PCA)–statistically uncorrelated optimal discriminant vectors (SUODV) method is put forward based on 3D patterns images. Firstly, the PCA is employed to condense the dimension of PD images, then uncorrelated discriminate features are extracted by improved SUODV algorithm, and the minimum distance classifier was constructed as classifier. The recognition results showed that this method can effectively classify four kinds of defects in GIS.