  Accurate wind turbine power curve models, which translate ambient conditions
into turbine power output, are crucial for wind energy to scale and fulfill its
proposed role in the global energy transition. While machine learning (ML)
methods have shown significant advantages over parametric, physics-informed
approaches, they are often criticised for being opaque 'black boxes', which
hinders their application in practice. We apply Shapley values, a popular
explainable artificial intelligence (XAI) method, and the latest findings from
XAI for regression models, to uncover the strategies ML models have learned
from operational wind turbine data. Our findings reveal that the trend towards
ever larger model architectures, driven by a focus on test set performance, can
result in physically implausible model strategies. Therefore, we call for a
more prominent role of XAI methods in model selection. Moreover, we propose a
practical approach to utilize explanations for root cause analysis in the
context of wind turbine performance monitoring. This can help to reduce
downtime and increase the utilization of turbines in the field.
