Insulation system health is crucial for reliable, lifelong operation of almost any electrical apparatus. While many studies have focused on the testing, modeling, and analysis of insulation aging mechanisms, research is needed to overcome new challenges in electric power systems. Fortunately, the progress in data analytics methods has opened up new opportunities to extract information from datasets. This study aims to make use of deep learning algorithms to lay the foundation for an online condition monitoring system that is capable of discriminating single- and multi-source corona discharges. In this article, we report the results of experimental testing and conversion of the data into phase-resolved partial discharge images, which we fed into deep neural networks. We begin by reviewing some of the most successful image recognition models including AlexNet, Inception-V3, residual network (ResNet), and DenseNet. Thereafter, we develop and optimize a ResNet model to achieve the highest accuracy model with the lowest computational cost.

中文翻译：

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一种识别单源和多源电晕放电的深度学习方法

绝缘系统的健康对于几乎所有电气设备的可靠、终身运行都至关重要。虽然许多研究都集中在绝缘老化机制的测试、建模和分析上，但仍需要进行研究以克服电力系统中的新挑战。幸运的是，数据分析方法的进步为从数据集中提取信息开辟了新的机会。本研究旨在利用深度学习算法为能够区分单源和多源电晕放电的在线状态监测系统奠定基础。在本文中，我们报告了实验测试结果并将数据转换为相位分辨局部放电图像，然后将其输入深度神经网络。我们首先回顾一些最成功的图像识别模型，包括 AlexNet、Inception-V3、残差网络 (ResNet) 和 DenseNet。此后，我们开发并优化了 ResNet 模型，以最低的计算成本实现最高准确度的模型。