小波算法消除突然三相短路试验数据的噪声  

Denoising of Test Data of Sudden Three-phase Short Based on Wavelet Algorithm

作　　者：徐明义[1] 

机构地区：[1]哈尔滨理工大学电气与电子工程学院,黑龙江哈尔滨150080

出　　处：《哈尔滨理工大学学报》2011年第2期118-121,共4页Journal of Harbin University of Science and Technology

摘　　要：为了消除动态试验数据中的噪声和奇异点对参数辨识精确度的影响,本文提出了基于小波分解在不同尺度噪声信号系数间不相关特性的小波区域相关性消噪方法.应用该方法对采集的发电机动态试验数据实施小波消噪,同时根据奇异点在小波分解时表现出的特性剔除试验数据中的奇异点,即试验数据的消噪和奇异点的剔除一步完成.并基于改进模型,应用LM算法高精确度辨识了一台650MW发电机参数.In order to eliminate noise and singularities from transient test data,this paper employs spatial correlation of wavelet coefficients to remove noise by features,in which noise-decomposed coefficients using wavelet transform are not correlative at different finest scales,and uses the characteristic of the coefficients through decomposing singularities to eliminate these singularities,that is denoising and deleting singularities in one step using wavelet algorithm.The parameters of a 650 MW turbo-generator are identified accurately by LM algorithm based on improved models.

关 键 词：突然三相短路 试验数据 小波算法 

分 类 号：TM921.51[电气工程—电力电子与电力传动]