Optimizing GIS partial discharge pattern recognition in the ubiquitous power internet of things context: A MixNet deep learning model

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.

Automated summary

Utilizing deep learning models for partial discharge pattern recognition in the power Internet of Things can improve fault diagnosis accuracy while significantly reducing computational and storage costs. The proposed method has shown a recognition accuracy of 99.1% after validation, demonstrating its advantages in feature extraction and visualization of model performance.