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 ...
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.