Transformer Architectures for Predictive Maintenance Content Generation in Smart Grid Education
Abstract
The integration of transformer architectures in smart grid education offers a promising avenue for predictive maintenance and adaptive content generation. By leveraging advanced deep learning models, educational systems can provide real-time, scenario-based training for grid operators, enhancing understanding of complex energy systems. However, existing approaches often rely on conventional machine learning or standard transformer models that primarily focus on historical sensor data, lacking the ability to incorporate domainspecific knowledge. These methods struggle with contextual understanding of interdependent components and fail to generate explainable educational content. To address these limitations, this study proposes a Knowledge-Enhanced Transformer for Contextual Predictive Maintenance (KE-T-PPM). The framework combines time-series sensor data with a smart grid knowledge graph, capturing both temporal dependencies and component relationships. This hybrid approach enables accurate prediction of equipment failures while producing explainable insights linked to operational knowledge. The KE-T-PPM framework is applied to generate adaptive educational content for smart grid operators, including interactive simulations, case studies, and preventive maintenance guidelines. By contextualizing predictive alerts within real-world scenarios, the system enhances operator comprehension and decision-making skills. Experimental evaluation demonstrates that the proposed method improves prediction accuracy, reliability, and clarity of training content compared to traditional models, highlighting its potential for smarter, knowledge-driven educational platforms in power systems.