  Recent years have seen an unprecedented growth in the use of sensor data to
guide wind farm operations and maintenance. Emerging sensor-driven approaches
typically focus on optimal maintenance procedures for single turbine systems,
or model multiple turbines in wind farms as single component entities. In
reality, turbines are composed of multiple components that dynamically interact
throughout their lifetime. These interactions are central for realistic
assessment and control of turbine failure risks. In this paper, an integrated
framework that combines i) real-time degradation models used for predicting
remaining life distribution of each component, with ii) mixed integer
optimization models and solution algorithms used for identifying optimal wind
farm maintenance and operations is proposed. Maintenance decisions identify
optimal times to repair every component, which in turn, determine the failure
risk of the turbines. More specifically, optimization models that characterize
a turbine's failure time as the first time that one of its constituent
components fail - a systems reliability concept called competing risk is
developed. The resulting turbine failures impact the optimization of wind farm
operations and revenue. Extensive experiments conducted for multiple wind farms
with 300 wind turbines - 1200 components - showcases the performance of the
proposed framework over conventional methods.
