Linear Discriminant Analysis and Adaptive Neuro-fuzzy Inference System Development of Gas Insulated Substation. Issue 5 (April 2020)

• Record Type: Journal Article
• Authors: Meng, Dedong
• Is Part Of: IOP conference series. Volume 474:Issue 5(2020)
• Publication Date: 2020-04
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/474/5/052020
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: In this study, a PD defect identification system based on linear discriminant analysis and adaptive neural-fuzzy inference system is developed. The partial discharge signs of the typical defects of GIS were measured with a measuring device. The corresponding characteristic parameters extracted from the gs were then filtered according to the optimization algorithm and stored in the form of three-dimensional discharge mode(q-φ-t). After that, the 3D discharge mode is transformed into statistical parameters. Statistical PD features can be used for defect classification purposes, but are obvious. Processing such a large database would be inefficient for that purpose. Therefore, it would be very helpful to select those features that 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 pre-processing techniques, which can not only reduce the computational complexity, but also achieve better classification performance by reducing irrelevant and redundant information in the data.