Title :
Online power quality disturbances identification based on data stream technologies
Author :
Kong, Yinghui ; Yuan, Jinsha ; Che, Linlin ; An, Jing
Author_Institution :
Dept. of Electron. & Commun. Eng., North China Electr. Power Univ., Baoding
Abstract :
Power quality disturbances identification is the important procedure for improving the power quality, and online application has actual value. An efficient method for power quality disturbances identification is presented in this paper. Wavelet decomposition is used for extracting the features of various disturbances, and decision tree in data mining is used for identifying 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 identify different disturbances in high accuracy and less time. Simulation experiment using several typical disturbances, swell, sag, interrupt, harmonic, transient impulse, transient oscillation, show the effectiveness of proposed method.
Keywords :
data mining; decision trees; power engineering computing; power supply quality; power system faults; wavelet transforms; data mining; data stream technologies; decision tree; one-pass scan algorithms; power quality disturbances identification; transient impulse; transient oscillation; wavelet decomposition; Classification tree analysis; Decision trees; Entropy; Filter bank; Low pass filters; Multiresolution analysis; Power engineering; Power harmonic filters; Power quality; Sorting; Power quality; data processing; feature extraction; identification; real time systems; wavelet transforms;
Conference_Titel :
Power Engineering Conference, 2007. IPEC 2007. International
Conference_Location :
Singapore
Print_ISBN :
978-981-05-9423-7
