中文摘要：

<正>The chaotic characteristics of time series of five partial discharge(PD) patterns in oil-paper insulation are studied.The results verify obvious chaotic characteristic of the time series of discharge signals and the fact that PD is a chaotic process.These time series have distinctive features,and the chaotic attractors obtained from time series differed greatly from each other by shapes in the phase space,so they could be used to qualitatively identify the PD patterns.The phase space parameters are selected,then the chaotic characteristic quantities can be extracted.These quantities could quantificationally characterize the PD patterns.The effects on pattern recognition of PRPD and CAPD are compared by using the neural network of radial basis function.The results show that both of the two recognition methods work well and have their respective advantages.Then,both the statistical operators under PRPD mode and the chaotic characteristic quantities under CAPD mode are selected comprehensively as the input vectors of neural network,and the PD pattern recognition accuracy is thereby greatly improved.

英文摘要：

The chaotic characteristics of time series of five partial discharge （PD） patterns in oil-paper insulation are studied. The results verify obvious chaotic characteristic of the time series of discharge signals and the fact that PD is a chaotic process. These time series have distinctive features, and the chaotic attractors obtained from time series differed greatly from each other by shapes in the phase space, so they could be used to qualitatively identify the PD patterns. The phase space parameters are selected, then the chaotic characteristic quantities can be extracted. These quantities could quantificationally characterize the PD patterns. The effects on pattern recognition of PRPD and CAPD are compared by using the neural network of radial basis function. The results show that both of the two recognition methods work well and have their respective advantages. Then, both the statistical operators under PRPD mode and the chaotic characteristic quantities under CAPD mode are selected comprehensively as the input vectors of neural network, and the PD pattern recognition accuracy is thereby greatly improved.