Optimization of maintenance planning in power distribution systems using a discrete-time Markov chain model asset analysis and resource allocation

This study aims to optimize maintenance planning in power distribution systems by addressing the complexity of operational states, resource allocation, and the high costs associated with preventive and corrective maintenance. A comprehensive state-based model is developed to represent all possible conditions a distribution asset may undergo, covering both preventive and corrective maintenance processes. A discrete-time Markov chain framework is applied to estimate the state transition probabilities, and using experimental data from the Distribution Company, the model quantifies the time assets spend in each state and the corresponding human resources required. Results demonstrate that the Markov-based approach provides reliable estimations of maintenance workloads and personnel needs, highlighting the significant share of resources devoted to preventive maintenance and quantifying total annual labor hours across asset categories. The method shows convergence and robustness after sufficient transition steps. Overall, the proposed model effectively captures maintenance dynamics, enabling planners to allocate resources more accurately. Utility companies can apply this framework to enhance cost-effectiveness through improved resource visibility, better system reliability, and informed decisions on maintenance crew allocation, spares management, and budgeting. • Developing a comprehensive state-based model to represent all possible conditions a distribution asset may undergo. • Estimating the probability of assets in each state using experimental data. • Applying a discrete-time Markov chain framework to estimate the probability of asset transitions across states. • Estimating the required human resources to handle the maintenance. • Optimizing maintenance planning in power distribution systems by addressing the complexity of operational states, resource allocation, and the high costs.