Open AccessPattern recognition of unknown partial discharge based on improved SVDD
Author(s) -
Gao Jiacheng,
Zhu Yongli,
Jia Yafei
Publication year - 2018
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2018.5249
Subject(s) - partial discharge , support vector machine , pattern recognition (psychology) , cluster analysis , artificial intelligence , fuzzy logic , computer science , class (philosophy) , data mining , mathematics , machine learning , voltage , engineering , electrical engineering
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.