Research on GIS partial discharge pattern recognition based on deep residual network and transfer learning in ubiquitous power internet of things context

As an important part of the power system, gas insulated switchgear (GIS) will cause serious failures once they break down, threatening the safety of the entire power grid. In the construction of the Ubiquitous Power Internet of Things (UPIoT), the intelligent terminal taking online monitoring as the means keeps the equipment fault samples and forms the sample database, which is of great significance to discover the latent insulation defects of GIS and take necessary measures in advance to ensure the safe and reliable operation of power grid. Aiming at the sample database provided by the intelligent terminal of the Internet of things, this paper proposes a method of GIS partial discharge (PD) using the depth residual network, which effectively improves the accuracy of model recognition. Although the comprehensiveness of the sample has been solved, as a transitional stage, the sample size is relatively small. Therefore, this paper uses transfer learning to solve the problem of high accuracy under the sample. In order to compare the state of art performance of the proposed method, some traditional convolutional networks such as LeNet, AlexNet, and VGG16 are used for comparison. After verification, the recognition accuracy of the deep residual network proposed in this paper is 94.6%, which is significantly higher than other models. At the same time, the parameter amount and storage space of the deep residual network are also significantly lower than those of other networks, further verifying that the model has a broad application space in the UPIoT context.