Open AccessPartial discharge signal denoising method based on frequency spectrum clustering and local mean decomposition
Author(s) -
Li Shanjun,
Sun Sashuang,
Shu Qin,
Chen Minwei,
Zhang Dakun,
Zhou Dianbo
Publication year - 2020
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.2020.0061
Subject(s) - partial discharge , white noise , noise reduction , hilbert–huang transform , noise (video) , signal (programming language) , cluster analysis , interference (communication) , signal to noise ratio (imaging) , pattern recognition (psychology) , computer science , decomposition , algorithm , artificial intelligence , mathematics , engineering , telecommunications , ecology , channel (broadcasting) , voltage , electrical engineering , image (mathematics) , biology , programming language
Suppressing the background noise of partial discharge (PD) is one of the key issues for accurately diagnosing the state of electrical equipment insulation. To solve this problem, this study proposes a new denoising method based on frequency spectrum clustering and local mean value decomposition. First, the K ‐means clustering is employed on the frequency spectrum to pick out narrow‐band interference frequencies. Next, the PD signal with white noise is decomposed by local mean decomposition into different product function components, and the components contain more information about time–frequency than the intrinsic mode functions originated from empirical mode decomposition. Besides, the adaptive threshold is utilised to eliminate white noise in the components. Finally, the denoised PD signal is synthesised by these denoised components. The proposed method and three traditional methods are applied to simulated and field‐detected noisy PD signals, respectively. The results of the evaluation indicators confirm that the proposed method is better than the existing PD denoising methods.