Open AccessA time‐domain statistical approach for harmonics separation and analysis
Author(s) -
He Chuan,
Shu Qin,
Liu Tianqi,
Han Xiaoyan
Publication year - 2017
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2239
Subject(s) - harmonics , time domain , robustness (evolution) , computer science , waveform , wavelet packet decomposition , wavelet , noise (video) , algorithm , electronic engineering , wavelet transform , voltage , artificial intelligence , engineering , telecommunications , electrical engineering , computer vision , biochemistry , chemistry , radar , image (mathematics) , gene
Summary The paper proposes a novel time‐domain statistical method to separate and analyze harmonics based on single channel analysis and principal component analysis. The proposed method separates the fundamental wave and harmonics to provide specific time‐domain information. Two identification methods are proposed to estimate the number of harmonics within the power system signal, even with high noise. This paper validates the effectiveness of the proposed method through mathematical deduction and studies the selection of Euclidean embedding dimension. The robustness of the proposed method is tested on several simulated synthetic signals and a real‐world signal. Simulation results show that the proposed method can detect harmonics with small amplitude in the presence of noise and has better separated waveform and less execution time than wavelet‐packet transform and empirical mode decomposition. It is also shown that the proposed method can detect sudden voltage changes and separate disturbances.