Optimization of Transformer Maintenance Schedules Based on Fault Prediction Models

In the pursuit of enhancing the reliability and costeffectiveness of power transformer maintenance, this paper introduces an optimized maintenance scheduling methodology based on Reinforcement Learning (RL). By modeling the maintenance scheduling problem as a Markov Decision Process (MDP), the proposed RL framework aims to minimize the cumulative operational costs while maximizing transformer reliability over time. Through a series of hypothetical simulations, the RL-optimized maintenance policy is compared against traditional time-based schedules, demonstrating a significant reduction in costs and an improvement in reliability. The results confirm the robustness and adaptability of the RL approach, highlighting its potential application in real-world transformer asset management.