  Wind turbine power curve models translate ambient conditions into turbine
power output. They are essential for energy yield prediction and turbine
performance monitoring. In recent years, increasingly complex machine learning
methods have become state-of-the-art for this task. Nevertheless, they
frequently encounter criticism due to their apparent lack of transparency,
which raises concerns regarding their performance in non-stationary
environments, such as those faced by wind turbines. We, therefore, introduce an
explainable artificial intelligence (XAI) framework to investigate and validate
strategies learned by data-driven power curve models from operational wind
turbine data. With the help of simple, physics-informed baseline models it
enables an automated evaluation of machine learning models beyond standard
error metrics. Alongside this novel tool, we present its efficacy for a more
informed model selection. We show, for instance, that learned strategies can be
meaningful indicators for a model's generalization ability in addition to test
set errors, especially when only little data is available. Moreover, the
approach facilitates an understanding of how decisions along the machine
learning pipeline, such as data selection, pre-processing, or training
parameters, affect learned strategies. In a practical example, we demonstrate
the framework's utilisation to obtain more physically meaningful models, a
prerequisite not only for robustness but also for insights into turbine
operation by domain experts. The latter, we demonstrate in the context of wind
turbine performance monitoring. Alongside this paper, we publish a Python
implementation of the presented framework and hope this can guide researchers
and practitioners alike toward training, selecting and utilizing more
transparent and robust data-driven wind turbine power curve models.
