Detection and Identification of Transient Power Quality Disturbance

DOI

10.2991/amcce-17.2017.30How to use a DOI?

Keywords

Disturbance signals, Wavelet transform, Generalized S transform, Rank-WSVM

Abstract

Aiming at insufficient classification performance and long classification time in detection and identification of transient power quality disturbance signal, this paper proposed a detection method that uses wavelet transform combined generalized S transform and an identification method named rank wavelet support vector machine(Rank-WSVM) which uses the complex Gaussian wavelet kernel function based on the original method for the detection and identification. Firstly, high and low frequency components are obtained by wavelet transform .then, the low frequency components were selected to through generalized S transform and extract feature vector. Finally achieving signal classification through Rank-WSVM which using complex Gaussian wavelet kernel function. Simulation results show that, the proposed method can improve the classification accuracy and can reduce the classification time.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Xiaohong Hao
AU  - Juan Cao
AU  - Yufang Han
AU  - Qun Gu
PY  - 2017/03
DA  - 2017/03
TI  - Detection and Identification of Transient Power Quality Disturbance
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017)
PB  - Atlantis Press
SP  - 173
EP  - 179
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-17.2017.30
DO  - 10.2991/amcce-17.2017.30
ID  - Hao2017/03
ER  -