Title :
Online Power Quality Disturbances Detection and Classification using One-Pass Wavelet Decomposition and Decision Tree
Author :
Kong, Ying-hui ; Yuan, Jin-sha ; An, Jing ; Che, Lin-Lin
Author_Institution :
North China Electr. Power Univ. No. 204, Baoding
Abstract :
An efficient method for power quality disturbances detection and classification is presented in this paper. Wavelet decomposition is used for extracting the features of various disturbances, and decision tree is used for classifying the disturbances. For online application, sliding window model and one-pass scan algorithms for wavelet decompositions are used. This method has low cost in memory and run time, it can detect and identify different disturbances in high accuracy and rapid speed. Simulation experiment using several typical disturbances, swell, sag, interrupt, harmonic, show the effectiveness of proposed method.
Keywords :
decision trees; distribution networks; power system management; wavelet transforms; decision tree; feature extraction; one-pass scan algorithms; one-pass wavelet decomposition; online power quality disturbance classification; online power quality disturbance detection; sliding window model; Classification tree analysis; Data mining; Decision trees; Feature extraction; Multiresolution analysis; Power quality; Signal resolution; Time frequency analysis; Wavelet analysis; Wavelet transforms; Data stream; Decision tree; Power quality disturbance; Sliding window; Wavelet decomposition;
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
DOI :
10.1109/ICMLC.2007.4370660
