Study on partial discharge pattern classification of GIS by adaptive neuro-fuzzy inference system

Abstract

Abstract In this study, a PD defect identification system based on linear discriminant a nalysis and adaptive neural fuzzy inference system is developed. in this study, the par tial discharge signs of typical defects of the GIS measurement were measured with a measuring device. the corresponding characteristic parameters extracted from the GIS measurement were filtered according to the optimization algorithm and stored in the form of three-dimensional discharge mode (q-φ-t). On this basis, the 3d discharge mode is converted into statistical parameters. statistical pd features can be used for defect classification purposes, but it is clear that for this goal, the treatment of sovolume data base will be inefficient. therefore, it would be very helpful to select those features t hat are more effective and more efficient to identify and distinguish potential pd defects through their relevant statistical parameters. Select feature extraction techniques for dimensionality reduction. In solving pattern classification problems, feature extraction methods are usually used as preprocessing techniques, which cannot only reduce the computational complexity, but also achieve better classification performance by reducing irrelevant and redundant information in the data.