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Recent years have seen an unprecedented growth in the use of sensor data toguide wind farm operations and maintenance. Emerging sensor-driven approachestypically focus on optimal maintenance procedures for single turbine systems,or model multiple turbines in wind farms as single component entities. Inreality, turbines are composed of multiple components that dynamically interactthroughout their lifetime. These interactions are central for realisticassessment and control of turbine failure risks. In this paper, an integratedframework that combines i) real-time degradation models used for predictingremaining life distribution of each component, with ii) mixed integeroptimization models and solution algorithms used for identifying optimal windfarm maintenance and operations is proposed. Maintenance decisions identifyoptimal times to repair every component, which in turn, determine the failurerisk of the turbines. More specifically, optimization models that characterizea turbine's failure time as the first time that one of its constituentcomponents fail - a systems reliability concept called competing risk isdeveloped. The resulting turbine failures impact the optimization of wind farmoperations and revenue. Extensive experiments conducted for multiple wind farmswith 300 wind turbines - 1200 components - showcases the performance of theproposed framework over conventional methods.

An Integrated Optimization Framework for Multi-Component Predictive Analytics in Wind Farm Operations & Maintenance