Bayesian Modelling of Multivalued Power Curves from an Operational Wind Farm
This addresses the need for better monitoring and maintenance in wind energy by providing a method to handle curtailed data, though it is incremental as it builds on existing probabilistic regression techniques.
The paper tackled the problem of modeling multivalued power curves in wind turbines due to power curtailments, which conventional regression cannot handle, and showed that their overlapping mixture model accurately represents practical data from an operational wind farm.
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.