  Power curves capture the relationship between wind speed and output power for
a specific wind turbine. Accurate regression models of this function prove
useful in monitoring, maintenance, design, and planning. In practice, however,
the measurements do not always correspond to the ideal curve: power
curtailments will appear as (additional) functional components. Such
multivalued relationships cannot be modelled by conventional regression, and
the associated data are usually removed during pre-processing. The current work
suggests an alternative method to infer multivalued relationships in curtailed
power data. Using a population-based approach, an overlapping mixture of
probabilistic regression models is applied to signals recorded from turbines
within an operational wind farm. The model is shown to provide an accurate
representation of practical power data across the population.
