The convolutional neural network (CNN) achieves excellent performance in pattern recognition owing to its powerful automatic feature extraction capability and outstanding classification performance. However, the actual samples obtained are unbalanced, and accurate diagnoses are difficult for the existing methods. A classification method for partial discharge (PD) pattern recognition in gas-insulated switchgear (GIS) that uses a generative adversarial network (GAN) and CNN on unbalanced samples is proposed. First, a novel Wasserstein dual discriminator GAN is used to generate data to equalise the unbalanced samples. Second, a decomposed hierarchical search space is used to automatically construct an optimal diagnostic CNN. Finally, PD pattern recognition classification in GIS of the unbalanced samples is realised by the GAN and CNN. The experimental results show that the GAN and CNN methods proposed in this study have a pattern recognition accuracy of 99.15% on unbalanced samples, which is significantly higher than that obtained by other methods. Therefore, the method proposed in this study is more suitable for industrial applications.

中文翻译：

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GAN 和 CNN 用于 GIS 中的不平衡局部放电模式识别

卷积神经网络（CNN）由于其强大的自动特征提取能力和出色的分类性能，在模式识别方面取得了优异的性能。然而，实际获得的样本是不平衡的，现有方法难以准确诊断。提出了一种在气体绝缘开关设备 (GIS) 中使用生成对抗网络 (GAN) 和 CNN 对不平衡样本进行局部放电 (PD) 模式识别的分类方法。首先，一种新颖的 Wasserstein 双鉴别器 GAN 用于生成数据以均衡不平衡的样本。其次，使用分解的分层搜索空间来自动构建最佳诊断 CNN。最后，通过GAN和CNN实现GIS中不平衡样本的PD模式识别分类。实验结果表明，本研究提出的GAN和CNN方法在不平衡样本上的模式识别准确率为99.15%，明显高于其他方法获得的准确率。因此，本研究提出的方法更适合工业应用。