  Wind farms are a crucial driver toward the generation of ecological and
renewable energy. Due to their rapid increase in capacity, contemporary wind
farms need to adhere to strict constraints on power output to ensure stability
of the electricity grid. Specifically, a wind farm controller is required to
match the farm's power production with a power demand imposed by the grid
operator. This is a non-trivial optimization problem, as complex dependencies
exist between the wind turbines. State-of-the-art wind farm control typically
relies on physics-based heuristics that fail to capture the full load spectrum
that defines a turbine's health status. When this is not taken into account,
the long-term viability of the farm's turbines is put at risk. Given the
complex dependencies that determine a turbine's lifetime, learning a flexible
and optimal control strategy requires a data-driven approach. However, as wind
farms are large-scale multi-agent systems, optimizing control strategies over
the full joint action space is intractable. We propose a new learning method
for wind farm control that leverages the sparse wind farm structure to
factorize the optimization problem. Using a Bayesian approach, based on
multi-agent Thompson sampling, we explore the factored joint action space for
configurations that match the demand, while considering the lifetime of
turbines. We apply our method to a grid-like wind farm layout, and evaluate
configurations using a state-of-the-art wind flow simulator. Our results are
competitive with a physics-based heuristic approach in terms of demand error,
while, contrary to the heuristic, our method prolongs the lifetime of high-risk
turbines.
