Abstract Gas-insulated switchgears (GISs) are an essential component of the power system, but in the event of a failure they may pose a serious threat to the safe operation of the entire power grid. The ubiquitous power Internet of Things (UPIoT), which is characterized by its online monitoring of failure samples for database building and further processing, is of great use in identifying potential insulation defects. We propose a MixNet deep learning model (MDLM) in the UPIoT context with the aim of optimizing partial discharge (PD) pattern recognition, after taking into account multiple indicators such as accuracy and effectiveness. Furthermore, a generative adversarial network was adopted for data enhancement to improve the model’s generalization ability and to solve such problems as noise jamming and the less clear effect of traditional spatial transformation methods on unified PD specification data. We found that an MDLM can effectively improve fault diagnosis accuracy while largely reducing calculation and storage costs. After validation, the recognition accuracy of an MDLM was 99.1%, significantly higher than that of other methods. The advantages of the proposed method were also demonstrated by the model feature extraction and the last hidden fully-connected layer using a visualization method.

中文翻译：

---

泛在电力物联网环境下优化GIS局部放电模式识别：一种MixNet深度学习模型

摘要 气体绝缘开关设备（GIS）是电力系统的重要组成部分，一旦发生故障，可能对整个电网的安全运行构成严重威胁。泛在电力物联网 (UPIoT) 的特点是可以在线监测故障样本以进行数据库构建和进一步处理，在识别潜在的绝缘缺陷方面非常有用。我们在 UPIoT 上下文中提出了 MixNet 深度学习模型 (MDLM)，目的是在考虑准确性和有效性等多个指标后优化局部放电 (PD) 模式识别。此外，采用生成对抗网络进行数据增强，提高模型的泛化能力，解决噪声干扰和传统空间变换方法对统一PD规范数据影响不明显等问题。我们发现 MDLM 可以有效提高故障诊断的准确性，同时大大降低计算和存储成本。经验证，一种MDLM的识别准确率为99.1%，明显高于其他方法。模型特征提取和最后一个隐藏的全连接层使用可视化方法也证明了所提出方法的优点。经验证，一种MDLM的识别准确率为99.1%，明显高于其他方法。模型特征提取和最后一个隐藏的全连接层使用可视化方法也证明了所提出方法的优点。经验证，一种MDLM的识别准确率为99.1%，明显高于其他方法。通过使用可视化方法提取模型特征和最后一个隐藏的全连接层，也证明了所提出方法的优点。