摘要
针对变压器内部常见绝缘缺陷,设计了尖刺、悬浮微粒、沿面、固体气隙等4种缺陷模型,利用超高频法获取了放电谱图。通过统计理论计算了PD超高频信号三维谱图的统计特征,以BP神经网络作为分类器,对所获得的PD信号进行了识别,取得了良好的效果,为开发变压器局部放电在线或离线分析系统奠定计算基础。
Partial discharge is an important feature of the characterization of transformer insulating state, but it also causes further deterioration of insulation. According to typical internal insulation defects, four kinds of defect modeles are designed, including Corona, suspended particulates, surface discharge and air gap in solid insulating material, the discharge spectra is obtained by UHF method. Features of discharge spectra are calculated by statistical theory, and with a BP neural network classifier to recognize PD signal, good result is achieved.
出处
《成都工业学院学报》
2013年第2期50-52,共3页
Journal of Chengdu Technological University
关键词
局部放电
超高频
绝缘缺陷判别
特征提取
神经网络
partial discharge（PD）
ultra frequency （UHF）
typical insulation defect
feature extraction
neural network
