【作者】 谢颜斌； 唐炬； 周倩； 高丽；

【Author】 Xie Yanbin Tang Ju Zhou Qian Gao Li (State Key Laboratory of Power Transmission Equipment & System Security and New Technology,Chongqing University,400044,China)

【机构】 重庆大学输配电装备及系统安全与新技术国家重点实验室；

【摘要】 UHF法作为GIS设备PD检测的有效方法已得到了广泛的应用,但GIS内UHF PD信号的特征提取一直是研究的难点问题。作者从小波包对UHF PD信号分解过程入手,根据已建立的GIS内4种典型缺陷UHF PD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UHF PD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UHF PD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,并最大限度的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UHF PD信号模式识别。更多还原

【Abstract】 Ultra-high frequency (UHF) method has been widely used for partial discharge (PD) detection in Gas insulated substation (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,the features space is constructed integrally;Kernel principal component analysis (KPCA) is also proposed for reducing dimension of features, dimension crisis is resolved well,and the divergence matrix strangeness in every class is eliminated,at the same time,the characteristics of signals is retained at the farthest.The classification results show that the features used in this paper are quite well for UHF PD defect identification.更多还原