【作者】 魏国忠；

【导师】 杜伯学；

【作者基本信息】 天津大学 ， 电力系统及其自动化， 2006， 硕士

【摘要】 近年来,通过测量局部放电(PD)信号对高压电气设备进行在线监测技术取得了很大的进步。局部放电在线监测系统中的放电类型的模式识别,能够及时发现绝缘内部局部缺陷及放电发展程度,防止事故发生。因此,通过分析放电脉冲特征评估电力设备的绝缘状况并正确识别出放电类型对于绝缘系统的诊断是一项重要工作。φ-q-n三维谱图(既指纹法,描述局部放电的重要特征量:相角φ,放电量q和单位时间内的放电次数n)被广泛的应用于局部放电类型的识别,并取得了很好的效果。但此方法建立在交流电压基础上,并不适用于直流高压系统;另外,采用“指纹法”或者其他的统计方法提取放电特征存在的问题是,特征量在很大程度上受电压值的影响,进而影响最终的识别结果.本文提出采用分析局部放电脉冲信号的时频特征的方法,通过小波理论,建立了表征局部放电脉冲信号的三维时频谱图,将局部放电信号时域脉冲波形展开为一个由时间、频率和振幅分量构成的三维空间,建立了反映局部放电脉冲信号特征的三维形象化模型。这个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,综合反映局部放电脉冲信号的时域和频域信息。文中采用了宽频带检测技术,构建了板-板放电、尖-板放电、尖-尖放电、内部放电和沿面放电几种典型的局部放电模型,模拟不同绝缘缺陷引起的局部放电,验证三维时频谱图分析方法对局部放电信号的识别处理效果。文中提出采用分形理论从所建立的三维时频谱图中提取放电特征,实现三维时频谱图的定量化描述,并构成识别特征量,采用反向传播算法(BP)神经网络用于局部放电信号类型的模式识别。实验结果表明,本方法可以有效的提取放电特征和识别放电类型。更多还原

【Abstract】 In recent years, great improvements have been made in the method of on-line monitoring high voltage power apparatus by means of measuring electrical partial discharge (PD). In this method, it is essential to investigate the characteristics of the PD pulses to evaluate electric insulation conditions. Recognition of PD types is fundamental for insulation system diagnosis. In the past decade, theφ-q-n PD pattern has been investigated for PD recognition and shown encouraging results. However, this pattern is not suitable for DC voltage application. Another prominent disadvantage of using this patterns or statistical operators is that it can be significantly influenced by the applied electric stress.In this research, a novel 3-dimensional pattern of partial discharge was set-up based on the theory of wavelet analysis. The PD pattern could reflect the discharge characteristic in both time-domain and frequency-domain. The parameters which characterize the PD were the frequency, the time and the energy.It is important to identify the type of these defects, e.g. internal discharge, surface discharge, corona, etc, so in this paper, five kinds of typical discharge models are designed and the relevant experiment methods are projected. In order to extract the PD pattern features, the authors investigated the application of fractal features to characterize the new 3-D PD patterns produced from different PD experimental models. PD pattern recognition scheme was put forward. In this scheme,PD fractal dimensions were used as feature vector, Back-propagation (BP) neural network was used as classifier. The discharge experiments have been carried out to validate the method with five types artificial discharge models. The results indicate that the proposed methodology has the recognizing and classifying ability for the PD types.更多还原