Open AccessA method of sample enhancement based on partial discharge PRPS spectrum
Author(s) -
Qi Tang,
Lingling Wu,
Rongbo Luo,
Guo-Wei Li,
Junbo Wang,
Xiaolong Li,
Shisong Yan
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1549/3/032089
Subject(s) - partial discharge , sample (material) , generalization , computer science , superposition principle , artificial intelligence , artificial neural network , pattern recognition (psychology) , noise (video) , algorithm , mathematics , engineering , physics , mathematical analysis , voltage , electrical engineering , image (mathematics) , thermodynamics
In this paper, we summarize the problems existing in the traditional partial discharge PRPS sample enhancement method, and propose a method based on the partial discharge PRPS spectrum sample enhancement, including threshold denoising, noise superposition, data density, data sparsity, phase shift, data enhancement method matrix and so on. It solves the problem that the difficulty of collecting the spectrum of partial discharge cases restricts the application of intelligent algorithms such as deep neural network. The practical application shows that the local partial discharge PRPS spectrum sample enhancement method can effectively enhance the sample size, make the sample distribution uniform and representative, and the deep learning neural network model trained by the sample set has high accuracy and generalization ability for PRPS spectrum diagnosis.