Detection and Identification of Transient Power Quality Disturbance
- 10.2991/amcce-17.2017.30How to use a DOI?
- Disturbance signals, Wavelet transform, Generalized S transform, Rank-WSVM
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.
- © 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/).
Cite this article
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 -