Open AccessApproximate‐derivative‐based signal‐processing method to segment power‐quality disturbances
Author(s) -
Akmaz Düzgün
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.0372
Subject(s) - segmentation , discrete wavelet transform , computer science , signal (programming language) , noise (video) , wavelet , noise reduction , pattern recognition (psychology) , signal processing , artificial intelligence , wavelet transform , digital signal processing , image (mathematics) , computer hardware , programming language
The increasing complexity of power systems and the increase in power quality (PQ) data have made it necessary to develop different and simple signal‐processing tools. In this study, an approximate‐derivative (AD) signal‐processing tool based on a simple mathematical processing approach was developed for the segmentation of PQ disturbances. Although the developed method was highly effective for the segmentation of noise‐free signals, the method was unable to properly handle the segmentation of noisy signals. Thus, to mitigate this situation, a denoising method based on the Sqtwolog threshold was applied to the noisy signals. After denoising, the proposed AD method effectively performed the segmentation. Subsequently, AD and a single‐level discrete wavelet transform (DWT) with Daubechies 4 mother wavelets were compared through simulations, which showed that successful results can be obtained using the proposed method. Furthermore, all simulations showed that the application of AD and single‐level DWT to PQ signals under different conditions resulted in similar patterns with different amplitudes. Therefore, this study provides a different approach for analysing signal using single‐level DWT.