电工技术学报
電工技術學報
전공기술학보
TRANSACTIONS OF CHINA ELECTROTECHNICAL SOCIETY
2010年
9期
35-40
,共6页
唐炬%谢颜斌%周倩%张晓星
唐炬%謝顏斌%週倩%張曉星
당거%사안빈%주천%장효성
局部放电%特征提取%最优小波包%核主分量分析
跼部放電%特徵提取%最優小波包%覈主分量分析
국부방전%특정제취%최우소파포%핵주분량분석
Partial discharge%feature extraction%best wavelet packet basis%kernel principal component analysis
UHF法作为GIS设备PD检测的有效方法已得到广泛应用,但GIS内UHFPD信号的特征提取一直是研究的难点问题。作者从小波包对UHFPD信号分解过程入手,根据已建立的GIS内4种典型缺陷UHFPD数学模型,分别采用熵最小原则选取最优小波包基,利用所得到的最优小波包基对UHFPD信号进行分解得到的小波包系数,计算信号在各频带投影序列的能量、在各个尺度下的模极大值和绝对平均值,构造出能完整描述UHFPD信号的特征空间,并用KPCA法将高维特征空间降到低维特征空间,解决了维数危机,消除了类内散度矩阵的奇异性,并最大限度地保持原有信号的特性。由此作为模式识别的特征量能够较好地应用于UHFPD信号模式识别。
UHF法作為GIS設備PD檢測的有效方法已得到廣汎應用,但GIS內UHFPD信號的特徵提取一直是研究的難點問題。作者從小波包對UHFPD信號分解過程入手,根據已建立的GIS內4種典型缺陷UHFPD數學模型,分彆採用熵最小原則選取最優小波包基,利用所得到的最優小波包基對UHFPD信號進行分解得到的小波包繫數,計算信號在各頻帶投影序列的能量、在各箇呎度下的模極大值和絕對平均值,構造齣能完整描述UHFPD信號的特徵空間,併用KPCA法將高維特徵空間降到低維特徵空間,解決瞭維數危機,消除瞭類內散度矩陣的奇異性,併最大限度地保持原有信號的特性。由此作為模式識彆的特徵量能夠較好地應用于UHFPD信號模式識彆。
UHF법작위GIS설비PD검측적유효방법이득도엄범응용,단GIS내UHFPD신호적특정제취일직시연구적난점문제。작자종소파포대UHFPD신호분해과정입수,근거이건립적GIS내4충전형결함UHFPD수학모형,분별채용적최소원칙선취최우소파포기,이용소득도적최우소파포기대UHFPD신호진행분해득도적소파포계수,계산신호재각빈대투영서렬적능량、재각개척도하적모겁대치화절대평균치,구조출능완정묘술UHFPD신호적특정공간,병용KPCA법장고유특정공간강도저유특정공간,해결료유수위궤,소제료류내산도구진적기이성,병최대한도지보지원유신호적특성。유차작위모식식별적특정량능구교호지응용우UHFPD신호모식식별。
Ultra-high frequency (UHF) method has been widely used for partial discharge (PD) detection in gas insulated switchgear (GIS),but the feature extraction for UHF PD signals is a difficult issue all the while. In this paper,a method using wavelet packet transform (WPT) is proposed to decompose the UHF PD signals,and the best basis is selected using minimum entropy criterion based on UHF PD mathematical model of four typical defects in GIS,then the energy in each frequency range,maximal values of module and absolute average values in each scale are computed according to WP coefficients,and the features space is constructed integrally; Kernel principal component analysis (KPCA) is also proposed for reducing dimension of features,and dimension crisis is resolved well,and the divergence matrix strangeness in every class is eliminated. At the same time,the characteristics of signals are retained the farthest. The classification results show that the features used in this paper are quite well for UHF PD defect identification.