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PD detection and recognition based on UHF method for typical models in air

机译:基于UHF方法的空气中典型模型的局部放电检测与识别

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The UHF method can not only be used in gas insulation substation-GIS and transformer, but also can be used to detect PD happening in air or other insulation dielectric. On account of the three kinds of PD happening in dielectric, out of dielectric and surface of dielectric respectively, four typical PD models were designed and manufactured with aluminium, namely, air gap, air void in epoxy, needle-flat and surface of epoxy, by which the PD experiments were carried out in air. The UHF PD signals were detected by the developed twin-spiral UHF coupler and amplifier with band of 100MHz–1500MHz. For every model, 50 UHF PD records were stored by digital storage oscilloscope with sampling rate 20GHz. After denoising with the improved wavelet adaptive threshold method, and envelope extraction, 19 features parameters were extracted in time domain and frequency domain to recognize the PD source type. Finally, a classifier was designed with 3-layers back propagation artificial neural networks, and the test results show that the extracted features and classifier are sufficient to recognize the PD source correctly with high robustness.
机译:UHF方法不仅可以用于气体绝缘变电站GIS和变压器,还可以用于检测空气或其他绝缘介质中发生的局部放电。针对电介质中发生的三种局部放电,分别从电介质和电介质表面中,设计和制造了四种典型的铝型PD模型,分别是气隙,环氧树脂中的气隙,针状平面和环氧树脂表面,通过PD实验在空气中进行。通过开发的双螺旋UHF耦合器和100MHz–1500MHz频带的放大器检测到了UHF PD信号。对于每个模型,数字存储示波器以50 GHz的采样率存储了50条UHF PD记录。在使用改进的小波自适应阈值方法进行去噪和包络提取之后,在时域和频域中提取了19个特征参数以识别PD源类型。最后,设计了具有三层反向传播人工神经网络的分类器,测试结果表明,所提取的特征和分类器足以正确识别PD源,且具有较高的鲁棒性。

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