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Condition monitoring measurements, such as vibration data, acoustic emission data, oil analysis data, power voltage and current data, etc., can be obtained from wind turbine components and be utilised to evaluate and predict the health conditions of the components and the turbines. The objective of condition-based maintenance (CBM) is to optimise the predictive maintenance activities based on the condition monitoring and prediction information to minimise the overall costs of wind power generation systems. In existing work, all the wind turbines are assumed to be of the same type and the lead times of different components are assumed to be constant. This is not the case in many practical applications. In this paper, we develop a CBM approach for wind turbine systems considering different types of wind turbines in a wind farm and different lead times for different turbine components, which lead to more accurate modelling of CBM activities in actual wind farms. In the proposed CBM approach, we present a new CBM policy involving two design variables for each turbine type, a method for turbine failure probability evaluation considering different lead times and a CBM cost evaluation method. Numerical examples are provided to demonstrate the proposed CBM approach.

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