  Wind farms are an indispensable driver toward renewable and nonpolluting
energy resources. However, as ideal sites are limited, placement in remote and
challenging locations results in higher logistics costs and lower average wind
speeds. Therefore, it is critical to increase the reliability of the turbines
to reduce maintenance costs. Robust implementation requires a thorough
understanding of the loads subject to the turbine's control. Yet, such
dynamically changing multidimensional loads are uncommon with other machinery,
and generally underresearched. Therefore, a multitiered approach is proposed to
investigate the load spectrum occurring in wind farms. Our approach relies on
both fundamental research using controllable test rigs, as well as analyses of
real-world loading conditions in high-frequency supervisory control and data
acquisition data. A method is introduced to detect operational zones in wind
farm data and link them with load distributions. Additionally, while focused
research further investigates the load spectrum, a method is proposed that
continuously optimizes the farm's control protocols without the need to fully
understand the loads that occur. A case of gearbox failure is investigated
based on a vast body of past experiments and suspect loads are identified.
Starting from this evidence on the cause and effects of dynamic loads, the
potential of our methods is shown by analyzing real-world farm loading
conditions on a steady-state case of wake and developing a preventive row-based
control protocol for a case of cascading emergency brakes induced by a storm.
