期刊
IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION
卷 18, 期 6, 页码 1897-1907出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TDEI.2011.6118628
关键词
Signal denoising; Wavelet transforms; Feature extraction; Support Vector Machines; Partial Discharge; Particle Swarm Optimization
The determination of particle type and dimensions in transformer oil is accomplished by using a Particle Swarm Optimization (PSO) technique in terms of the features extracted from the measured partial discharge (PD) pulse patterns. PSO selection of effective features is shown to be successful with intelligent classification for both electrical and acoustically measured data. Classification results of individual measurements were also reliable and far surpassed the efficiency of classification results obtained using the classifier solely for the same dimension of input features. The approach in this paper provides a solid basis for a data mining technique that can be used for the interpretation of both time and phase resolved raw PD patterns by searching a wide range of statistical attributes.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据