Open AccessPartial discharge of white noise suppression method based on EEMD and higher order statistics
Author(s) -
Wang Wei,
Peng Weiwen,
Tian Muqin,
Tao Wenbiao
Publication year - 2017
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2017.0688
Subject(s) - hilbert–huang transform , kurtosis , white noise , partial discharge , thresholding , additive white gaussian noise , estimator , higher order statistics , gaussian noise , transformer , gaussian , noise (video) , mathematics , statistics , computer science , pattern recognition (psychology) , speech recognition , algorithm , artificial intelligence , signal processing , electronic engineering , engineering , physics , electrical engineering , digital signal processing , voltage , image (mathematics) , quantum mechanics
The insulation deterioration of mining dry‐type transformer is always accompanied with partial discharge (PD). The assessment accuracy of transformer insulation performance is directly affected by the authenticity of the PD signal. Based on the ensemble empirical mode decomposition (EEMD) and combining the principle of higher order statistics, a hybrid method for suppressing white noise in PD signals was proposed. First, the PD signals were decomposed into a number of intrinsic mode function (IMF) components. Then the signal's Gaussian components were detected and removed by kurtosis as gaussianity estimators supplemented by bootstrap techniques. The white noise in the components were suppressed through thresholding and reconstructing each IMF with adaptive thresholds. Finally, the results were reconstructed and return to the original signal. The validity of this method is proved by simulated signals and practical PD signals.