The construction of the ubiquitous power internet of things (UPIoT) provides a new feasible solution for gas-insulated switchgear (GIS) online monitoring and fault diagnosis, but it also puts forward greater requirements for time and accuracy. How to find an effective real-time model that can be applied to the UPIoT mobile terminals has become an urgent problem needing to be solved. To this end, this study proposes a lightweight convolutional neural network (LCNN) for GIS partial discharge (PD) pattern recognition using three lightweight convolutional blocks, and introduces the lowest recognition accuracy of single-class faults as the primary indicator for selecting the optimal model under the UPIoT. First, three lightweight convolutional blocks are introduced for constructing an LCNN. Then, the optimal model constructed by the lightweight blocks is sought. Next, criteria for determining the best model are introduced, and the best model under the UPIoT is selected. This study provides a reference standard for the construction of GIS PD pattern recognition under the UPIoT. Meanwhile, through the balance of evaluation indicators, this study verifies that the minimum recognition accuracy of the MnasNet model is 98.8%, which is obviously better than other methods and lays a solid foundation for GIS PD pattern recognition.

中文翻译：

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通用电力物联网环境中通过轻型卷积神经网络的GIS局部放电模式识别

无处不在的电力物联网（UPIoT）的建设为气体绝缘开关设备（GIS）的在线监测和故障诊断提供了一种新的可行的解决方案，但也对时间和准确性提出了更高的要求。如何找到可以应用于UPIoT移动终端的有效实时模型已经成为亟待解决的问题。为此，本研究提出了一种轻量级卷积神经网络（LCNN），用于使用三个轻量级卷积块进行GIS局部放电（PD）模式识别，并介绍了单类故障的最低识别精度作为选择最佳模型的主要指标在UPIoT下。首先，引入了三个轻量级的卷积块来构造LCNN。然后，寻找由轻量级块构造的最佳模型。接下来，介绍确定最佳模型的标准，并选择UPIoT下的最佳模型。该研究为UPIoT环境下GIS PD模式识别的构建提供了参考标准。同时，通过评估指标的平衡，验证了MnasNet模型的最小识别精度为98.8％，明显优于其他方法，为GIS PD模式识别奠定了坚实的基础。