Multi-source partial discharge diagnosis in gas-insulated switchgear via zero-shot learning

Partial discharge (PD) diagnosis in gas insulation switchgear (GIS) has been continuously studied as a hot issue. The diagnostic model developed at this stage requires the possible combination of all single PDs, and each combination has sufficient data. However, the demand for the number of multi-source PD samples increases in the form of an index due to the accidental and concurrency of different types of PD, where it is unrealistic to collect sufficient PD samples. To this end, we proposed zero-shot learning (ZL) to diagnose multi-source PD. The ZL diagnosis multi-source PD only required single PD for training. Specifically, an attention residual network was introduced for feature extraction. Then, a novel semantic descriptor was proposed to obtain the semantic attributes, and the semantic embedded features were mapped to the visual space with a full connection network. Finally, the relationship network adopted the similarity between the visual features and semantic embedded features, and the multi-source PD diagnosis was realized. In addition, the multi-source PD experiment in GIS was constructed to verify the proposed ZL. The experimental results demonstrated that the ZL could achieve 90.25 % accuracy when only single PD participated in the training. It provided novel solutions for the diagnosis of multi-source PD in GIS.