基于神经网络的局部放电模式识别方法研究

Research of pattern recognition method of partial discharge signals based on neural network

局部放电是评价在线高压电器绝缘状态的重要技术参数之一。绝缘缺陷和局部放电紧密关联,在线监测高压电器运行状态,实时采集绝缘局部放电信号并对其进行数理分析处理和属性分类,推断、预测绝缘缺陷部位及放电发展程度,可以预报预防事故发生。因此,局部放电模式识别技术的研究和理论探讨具有重要的工程应用价值和学术意义。因此,文中将神经网络应用于局部放电的模式识别并进行研究,对数据进行归一化处理后得到较好的网络收敛性和识别速度。

Partial discharge （PD） is one of important technical parameters which are used to assess the insulation state of high voltage electrical apparatus on-line. The relation of insulation defects with PD sources is closely interrelated so that operating states of high voltage electrical apparatus can be clearly monitored,and the insulation failures can be simultaneously diagnosed, predicted, and the locations of insulation defects and the deteriorated degrees could be deduced logically from the PD data-base mined real time. Therefore, theoretical study and scientific experiment on pattern recognition method of PD signal waveform not only have important academic significance, but have great practical application values. So in this paper wavelet neural network is used to partial discharge pattern recognition and studied, after dealing with the data, astringency of net and speed of recognition are acquired.