Identification of contaminating particle geometry in a co-axial configuration using trichel pulse characteristics

M. E. El Bahy, S. Ward, R. Morsi, M. Badawi
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引用次数: 2

Abstract

A method is presented for detecting and identifying the shape and size of a contaminating metallic particle in gas insulated system (GIS). A fixed particle-initiated negative corona in air insulated co-axial cylindrical configuration is investigated at a voltage slightly above the corona onset level. The characteristics of the negative corona pulses (Trichel pulses) are calculated by mathematical modeling the process taking place during the negative corona discharge. An experimental set-up is built up to measure the Trichel pulse characteristics and to check the accuracy of the present calculation. The calculated values of corona Trichel pulse amplitudes and repetition rates agree well with the measured values. So, the Trichel pulse characteristics are calculated for different particle sizes and shapes. These data of pulses are used as a bias for designing and training the artificial neural network technique (ANN). For earlier detection of insulation defect, the Trichel pulse characteristics are measured and then given as an input data to the trained ANN. Then, we could identify the particle shape and size.
利用三头脉冲特性在同轴结构中识别污染粒子的几何形状
提出了一种检测和识别气体绝缘系统中污染金属颗粒形状和大小的方法。在略高于电晕起始水平的电压下,研究了空气绝缘同轴圆柱形结构中固定粒子引发的负电晕。通过对负电晕放电过程的数学建模,计算了负电晕脉冲(Trichel脉冲)的特性。建立了一个实验装置来测量Trichel脉冲特性,并检验了计算的准确性。电晕Trichel脉冲振幅和重复频率的计算值与实测值吻合较好。因此,计算了不同粒径和形状的颗粒的Trichel脉冲特性。这些脉冲数据被用作设计和训练人工神经网络技术(ANN)的偏差。为了更早地检测绝缘缺陷,测量了Trichel脉冲特性,然后将其作为训练后的人工神经网络的输入数据。然后,我们可以确定颗粒的形状和大小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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