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We examine optimal repair strategies for wind turbines operated under stochastic weather conditions. In-situ sensors installed at wind turbines produce useful information about the physical conditions of the system, allowing wind farm operators to make informed decisions. Based on the information from sensors, our research objective is to derive an optimal preventive maintenance policy that minimizes the expected average cost over an infinite horizon. Specifically, we formulate the problem as a partially observed Markov decision process. Several critical factors, such as weather conditions, lengthy lead times, and production losses, which are unique to wind farm operations, are considered. We derive a set of closed-form expressions for the optimal policy, and show that it belongs to the class of monotonic four-region policies. Under special conditions, the optimal policy also belongs to the class of monotonic three-region policies. The structural results of the optimal policy reflect the practical implications of the turbine deterioration process.