Season-Dependent Condition-Based Maintenance for a Wind Turbine Using a Partially Observed Markov Decision Process

We develop models and the associated solution tools for devising optimal maintenance strategies, helping reduce the operation costs, and enhancing the marketability of wind power. We consider a multi-state deteriorating wind turbine subject to failures of several modes. We also examine a number of critical factors, affecting the feasibility of maintenance, especially the dynamic weather conditions, which makes the subsequent modeling and the resulting strategy season-dependent. We formulate the problem as a partially observed Markov decision process with heterogeneous parameters. The model is solved using a backward dynamic programming method, producing a dynamic strategy. We highlight the benefits of the resulting strategy through a case study using data from the wind industry. The case study shows that the optimal policy can be adapted to the operating conditions, choosing the most cost-effective action. Compared with fixed, scheduled maintenances and a static strategy, the dynamic strategy can achieve the considerable improvements in both reliability and costs.