Bayesian modelling of multivalued power curves from an operational wind farm. (15th April 2022)

• Record Type: Journal Article
• Authors: Bull, L.A.; Gardner, P.A.; Rogers, T.J.; Dervilis, N.; Cross, E.J.; Papatheou, E.; Maguire, A.E.; Campos, C.; Worden, K.
• Is Part Of: Mechanical systems and signal processing. Volume 169(2022)
• Publication Date: 2022-04-15
• Subjects: Wind energy -- Power curve -- Performance monitoring -- Structural health monitoring -- Gaussian processes -- Power curtailment; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108530
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: 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. Highlights: A novel mixture of Gaussian process regressors is used to model wind turbine power curves. The model captures operational wind-power relationships, including curtailments. Therefore, curtailment data is automatically modelled rather than filtered out. A population-based model is inferred to represent the behaviour across the wind farm.