A multi-task learning framework for diagnosing partial discharge types and assessing severity

Partial Discharge (PD) is one of the most critical factors contributing to the degradation of insulation systems in power transformers. Early detection, accurate localization, identification of discharge types, and assessment of discharge severity play a significant role in improving system reliability and reducing maintenance costs. In recent years, various approaches based on electromagnetic waves, acoustic emissions, frequency-domain analysis, wavelet transforms, and artificial intelligence techniques have been proposed for PD monitoring and diagnosis. However, most existing studies focus on a single task and rarely address multiple diagnostic objectives within a unified framework. This paper proposes a novel framework, termed PD-IntelliFusionNet, for simultaneous PD type diagnosis and severity assessment. The proposed framework integrates acoustic and electromagnetic sensing, physical modeling, multi-scale feature extraction, and multi-task deep learning into a unified architecture. First, acoustic emission and ultra-high-frequency signals are collected from multiple sensors. Subsequently, time-domain, frequency-domain, and wavelet-based features are extracted and combined with deep representations learned automatically by neural networks. A multi-task learning architecture equipped with an attention mechanism is then employed to perform PD type classification and severity assessment simultaneously. Furthermore, the generated diagnostic outputs can be utilized as inputs to an intelligent reinforcement learning-based decision-making system for condition monitoring and maintenance planning. Previous studies have demonstrated that the fusion of acoustic and electromagnetic information improves diagnostic accuracy and robustness. In addition, multi-task learning architectures enhance severity assessment performance by exploiting shared knowledge among related tasks. The proposed PD-IntelliFusionNet framework provides a comprehensive and scalable solution for intelligent condition monitoring of power transformers and offers a promising direction for next-generation PD diagnostic systems.