Deep Learning-Based Multi-Source Partial Discharge Pattern Recognition Integrated with Auxiliary Prior Localization Information

Partial discharge accelerates insulation degradation and is a major contributing factor to insulation failures in electrical equipment. Effectively detecting partial discharge signals and accurately identifying discharge types are crucial for assessing insulation conditions. However, in multi-source partial discharge scenarios, signal overlap in the time domain significantly complicates diagnosis. Furthermore, the characteristics of UHF signals received by sensors are influenced by both the type of insulation defects and the propagation path between the discharge sources and the sensors. Therefore, incorporating prior localization information is essential for accurate discharge type identification. This paper addresses the challenges of multi-source partial discharge type identification under repetitive pulse excitation by proposing a deep learning model. First, the Time-Reassigned Multi-Synchrosqueezing Transform (TMSST) is employed to mitigate the impact of signal overlap on feature extraction. Robust features are then extracted using the ResNeSt model with its Split-Attention mechanism. Considering the influence of UHF propagation paths on the received signal characteristics, prior localization information is encoded as auxiliary features and integrated with the time-frequency features. Finally, the Transformer Encoder backbone is utilized to effectively capture dependencies between time-frequency features and prior localization knowledge, enhancing both accuracy and interpretability. Experimental results demonstrate that the proposed model achieves an mAP of 98.7%, outperforming classical deep learning methods, and exhibits strong robustness to noise, highlighting its potential for engineering applications in multi-source partial discharge identification.