Revolutionizing Lifespan Prediction and Cumulative Damage Assessment of XLPE Copper Main Cables Using Multiphysics Simulation and Intelligent AI: A Case Study of the Industrial Technician School Building, RMUTSV
10.14416/j.ind.tech.2025.12.009
Keywords:
Thermal Fatigue, XLPE copper main cables, Multiphysics Simulation, achine Learning PredictionAbstract
This study investigates the thermal fatigue behavior of XLPE copper main power cables within the electrical distribution system of the Industrial Technician School Building at RMUTSV. Real-time measurements of temperature, current, and voltage were collected over a one-month period, revealing significant thermal fluctuations in the main conductors. Multiphysics simulation results indicated that Phase B exhibited the highest mean temperature of 30.82°C-approximately 12% greater than the other phases-leading to a maximum voltage drop of 1.40% and a peak energy loss of 0.00485W under high-load conditions. The copper conductor in Phase B also experienced thermal stress reaching up to 85% of its critical limit. In addition, a Machine Learning model developed in this research achieved 92% accuracy in predicting thermal fatigue risk. The results contribute to proactive maintenance planning and optimized load management, effectively reducing energy losses and extending the service life of XLPE copper cables. Overall, this work represents a significant advancement toward intelligent, reliable, and energy-efficient electrical infrastructure in real-world operational environments.
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