Pattern recognition of unknown partial discharge based on improved SVDD. Issue 7 (1st October 2018)

• Record Type: Journal Article
• Authors: Gao, Jiacheng; Zhu, Yongli; Jia, Yafei
• Is Part Of: IET science, measurement & technology. Volume 12:Issue 7(2018)
• Page Start: 907
• Page End: 916
• Publication Date: 2018-10-01
• Subjects: support vector machines -- pattern recognition -- data description -- pattern classification -- learning (artificial intelligence) -- pattern clustering -- partial discharges -- electrical engineering computing; improved SVDD -- unknown PD types -- pattern recognition -- unknown partial discharge -- insulation condition -- electric equipment -- improved support vector data description algorithm -- Otsu algorithm -- one‐class classification methods -- one‐class support vector machine -- probability density function estimation -- tritraining algorithm -- artificial defect models -- SVDD classifiers; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2018.5249
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : The pattern recognition of a partial discharge (PD) is critical to evaluate the insulation condition of electric equipment of high voltage. However, much attention had been paid to recognise PD types which are known, but it is ignored that the types which did not appear previously. To solve the above problems, a method to recognise unknown PD types based on improved support vector data description (SVDD) algorithm is introduced in this study. Tri‐training algorithm and double thresholds set based on Otsu algorithm are used to improve the traditional SVDD classifiers. PD samples collected from different artificial defects models are finally classified by the improved fuzzy c‐means clustering algorithm. Experiments compared the improved SVDD with existing one‐class classification methods such as SVDD, one‐class support vector machine and probability density function estimation. The results show that the proposed method has much higher recognition accuracy. It is verified that the improved SVDD is an efficient method which can be applied to the recognition of unknown PD types.