Season-Dependent Condition-Based Maintenance for a Wind Turbine Using a Partially Observed Markov Decision Process
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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. 2006 IEEE.
