G. S. Hu, F. F. Zhu and Z. Ren, “Power Quality Disturbance Identification Using Wavelet Packet Energy Entropy and Weighted Support Vector Machines,” Expert Systems with Applications, Vol. 35, No. 1-2, 2008, pp. 143-149.

has been cited by the following article:

• TITLE: Classification of Power Quality Disturbances Using Wavelet Packet Energy Entropy and LS-SVM

  AUTHORS: Ming Zhang, Kaicheng Li, Yisheng Hu

  KEYWORDS: Power Quality (PQ), Wavelet Packet Transform (WPT), Wavelet Packet Log-Energy Entropy, Least Square Support Vector Machines (LS-SVM)

  JOURNAL NAME: Energy and Power Engineering, Vol.2 No.3, August 25, 2010

  ABSTRACT: The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. In this paper, there were two stages in analyzing PQ signals: feature extraction and disturbances classification. To extract features from PQ signals, wavelet packet transform (WPT) was first applied and feature vectors were constructed from wavelet packet log-energy entropy of different nodes. Least square support vector machines (LS-SVM) was applied to these feature vectors to classify PQ disturbances. Simulation results show that the proposed method possesses high recognition rate, so it is suitable to the monitoring and classifying system for PQ disturbances.