摘要
根据小波理论建立了表征局部放电脉冲信号的三维时频谱图,该三维谱图综合反映了局放脉冲信号的3个基本特征:时间分量、频率分量和放电能量的分布。采用了分形理论从所建立的三维时频谱图中提取放电特征,并构成识别特征量,采用误差反传神经网络对局部放电信号的类型进行模式识别。试验结果表明,该方法可有效区分局部放电的类型。
On the basis of wavelet analysis a threedimensional time-frequency pattern to characterize partial discharge （PD） impulse signal is upbuilt which comprehensively shows three basic features of PD impulse signals： time component, frequency component and distribution of discharging energy. From the upbuilt three-dimensional pattern the discharge features are extracted with fractal theory, thus PD fractal dimensions are used as feature vectors. The pattern recognition of the type of PD signal is conducted by means of BP neural network. The discharge experiments have been conducted to validate the proposed method with five types artificial discharge models, and experiment results show that the proposed method can effectively distinguish the type of PD.
出处
《电网技术》
EI
CSCD
北大核心
2006年第13期76-80,共5页
Power System Technology
关键词
局部放电
特征提取
分形理论
模式识别
高电压绝缘技术
partial discharge
feature extraction
fractal theory
pattern recognition
high voltage insulation engineering
