Several studies for partial discharge (PD) pattern recognition using artificial neural network (ANN) were reported in the early 1990s. Usually, in an actual field such as a substation, data on partial discharge is scarcely available, or even rare. In many cases, the power supply phase required for the PRPD pattern cannot be easily obtained. We propose an ANN method that shifts the phase in which the maximum signal intensity detected with PD sensors is generated and used it as training and input data, even for the few phases resolved PD data available in the field. This ANN method was applied to the PRPD pattern obtained in a practical field. As a result, it was shown that the discrimination rate between PD and noise was improved, and therefore the proposed ANN method was found to be effective.

中文翻译：

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基于相位分辨局部放电模式重建的神经网络检查变电站绝缘诊断

1990 年代初报道了一些使用人工神经网络 (ANN) 进行局部放电 (PD) 模式识别的研究。通常，在变电站等实际现场，局部放电的数据很少，甚至很少。在许多情况下，无法轻易获得 PRPD 模式所需的电源相位。我们提出了一种 ANN 方法，该方法改变了产生 PD 传感器检测到的最大信号强度的相位，并将其用作训练和输入数据，即使对于现场可用的少数相位解析 PD 数据也是如此。该 ANN 方法应用于实际领域中获得的 PRPD 模式。结果表明，PD 和噪声之间的辨别率得到提高，因此发现所提出的 ANN 方法是有效的。