基于时间序列对油纸绝缘局部放电模式识别的研究

Pattern Recognition of Partial Discharge in Oil-paper Insulation Based on Time Series

文中制作了5种典型的油纸绝缘局部放电模型,从局部放电的测量结果中提取出局部放电幅值的时间序列,对放电脉冲幅值的时间序列进行预处理,运用自回归模型对预处理的序列进行拟合,并将拟合所得的模型系数作为局部放电模式识别的特征向量,运用BP神经网络对这5种放电模型进行模式识别。笔者运用不同阶的自回归模型对局部放电脉冲幅值序列进行拟合,并在各阶的情况下分别对局部放电进行模式识别。结果表明,在运用4阶或6阶滞后模型对局部放电进行拟合时,能获得较高的正判率,均达到了80%以上。

Five typical partial discharge（PD） models of oil-paper insulation are made, and the time series of the models＇ PD pulse amplitude are extracted from the measurements. Then the time series are pre-processed and further fitted by the auto regressive（AR） model, and the coefficients of the AR model are taken as feature vector for pattern recognition of the five partial discharge models by BP neural network. The AR models with different order are adopted to fit the time series, and higher recognition accuracy of over 80% is achieved by the 4th-or 6th-order AR model.