【作者】 李武峰；

【导师】 王伟；

【作者基本信息】 华北电力（北京）大学 ， 高电压与绝缘技术， 2002， 硕士

【摘要】 大型发电机定子局部放电的在线监测是诊断发电机定子绝缘故障的有效方法。本文用发电机定子模型线棒研究了发电机中的三种类型的放电：内部放电、端部放电和槽间放电脉冲的时域特性和频域特性，讨论了在高压端检测时发电机定子局部放电在线监测装置的检测频带。然后本文利用自行设计的局部放电高频在线监测系统和一种局部放电低频在线监测系统（PDD-4）研究了发电机中单一类型和复合类型的局部放电的统计特征，研究表明发电机上的这三种局部放电具有明显不同的统计特征，且各种局部放电在高频和低频两种不同的在线监测系统下的检测结果都具有相似性。最后本文用局部放电的分布谱图及其统计特征参数和BP神经网络进行了发电机定子局部放电的模式识别。研究表明，发电机定子的几种不同类型的局部放电具有一些不同的特征，通过局部放电的分布谱图及其统计特征参数和神经网络可以比较好地识别发电机定子局部放电的类型。更多还原

【Abstract】 The on-line monitoring of partial discharge on large generator stator windings is an effective way to diagnose the insulation condition of stator windings. In this paper the time features and the frequency features of three different types of partial discharge (PD) occurred on generators, such as delamiantion PD, end winding PD and slot PD, were analyzed based on the laboratory test of generator stator winding model bars. The frequency band of the detector for on-line monitoring partial discharges on generators was discussed. A high frequency setup (HF) and a new low frequency (LF) partial discharge detector named PDD-4 were used to investigate the features of single pattern partial discharges and composite pattern partial discharges on large generators. The test results showed that the three different types of partial discharge on generator stator windings had different statistical features and the features under the two different detectors were similar. The spectrums and their statistical parameters of different types of partial discharge were used to study on the pattern recognition of partial discharges on generators by a BP neural network. The research shows that the three different types of partial discharge on generators have very different features and we can recognize their patterns using the spectrums and their statistical parameters by neural network quite well.更多还原