The integration of Artificial Intelligence (AI) into predictive maintenance for smart grid components has revolutionized the reliability and efficiency of modern power systems. This study explores advanced AI-based predictive maintenance models designed to anticipate failures and optimize maintenance schedules for key smart grid assets such as transformers, circuit breakers, and sensors. By leveraging machine learning algorithms and real-time data analytics, these models enable early fault detection, reduce downtime, and lower operational costs. The implementation of AI-driven predictive maintenance in smart grids supports enhanced grid stability, improved asset lifespan, and sustainable energy management. Challenges and future directions for AI applications in smart grid maintenance are also discussed to promote resilient and intelligent power networks.
V et al. (Thu,) studied this question.