Open AccessThe Inspection of Power Quality Disturbances by Using Improved Extreme-value Lifting Morphological Wavelet Method
Author(s) -
Huaying Zhang,
Ziheng Hu,
Yan Li,
Jingwen Ai
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/486/1/012022
Subject(s) - wavelet , maxima and minima , extreme value theory , operator (biology) , pattern recognition (psychology) , computer science , artificial intelligence , mathematics , noise (video) , algorithm , statistics , mathematical analysis , image (mathematics) , biochemistry , chemistry , repressor , transcription factor , gene
For solving the difficulty of extracting the disturbing characteristics of the signals in power quality processing, and restraining the noise in the process of sampling, an improved extreme-value lifting morphological wavelet method is constructed. Firstly, morphological extreme-value operator is chosen to be the prediction operator and update operator, then the max-lifting and min-lifting morphological wavelets are constructed. Secondly, the improved extreme-value lifting morphological wavelet method is designed to further highlight local maxima and minima information of the disturbing signals based on the first step. Finally, the signals are dealt with this method, and the detail coefficients can be obtained, which have kept the disturbing characteristics of the signals. The result of simulation and analysis shows that this algorism owns the trait of simple, exactness, and de-noising.