典型SF_6气体绝缘缺陷局部放电脉冲的Gabor特征识别  

Identification of Gabor Distribution Characteristics of Partial Discharge on Typical SF_6 Gas Insulated Defects

作　　者：任重 

机构地区：[1]深圳供电局有限公司,广东深圳518001

出　　处：《高压电器》2015年第12期142-148,共7页High Voltage Apparatus

摘　　要：局部放电脉冲具有非平稳特性,采用联合时频域分析比常规的单域分析更能诊断放电的特征。因此,针对现场GIS可能存在的缺陷类型,设计了针板、悬浮和空穴的缺陷模型,提取其在工频电压下局部放电脉冲的Gabor分布特征,并采用BP人工神经网络对特征参数进行训练和测试;同时通过现场110 kV GIS的缺陷的局部放电实测,对放电脉冲Gabor特征分析的有效性进行了研究。结果表明,放电脉冲的时频特征图谱能够得到有效提取,为放电评估提供参考,特征参数的训练识别率和测试识别率较高,利于放电类型的识别。Due to the non-stationary property of partial discharge（PD） signals, the joint time-frequency domain analysis of discharge pulses can reflect the discharge characteristics more perfectly compared with time domain or frequency domain analysis. Hence, in this study a needle-plate defect model, a floating defect model and an internal cavity defect model were designed according to possible defects in actual GIS. Gabor distribution characteristics of PD pulses of different defects under power frequency voltage were obtained, and the characteristic parameters were input to a back propagation artificial neural network for training and test. The validity of Gabor characteristic analysis on discharge pulses was analyzed through field PD test of 110 kV GIS defects. The results showed that the time-frequency spectrograms of the discharge pulses were extracted effectively for discharge assessment, and the training and test identification rates of the characteristic parameters were high enough for identification of discharge types.

关 键 词：局部放电 Gabor分布 SF6 缺陷类型 时频 

分 类 号：TM595[电气工程—电器]