Data-driven multivariate power curve modeling of offshore wind turbines. (October 2016) - Janssens, Olivier; Noppe, Nymfa; Devriendt, Christof; Walle, Rik Van de; Hoecke, Sofie Van
Abstract: Performance monitoring of offshore wind turbines is an essential first step in the condition monitoring process. This paper provides three novelties regarding power curve modeling. The first consists of illustrating that univariate power curve modeling can be improved by the use of non-parametric methods such as stochastic gradient boosted regression trees, extremely randomized forest, random forest, K-nearest neighbors, and the method of bins according to the IEC standard 61, 400–12-1. This is confirmed on both a synthetic data set and a real live data set containing data from three offshore wind turbines. The second novelty consists of an improvement regarding overall power curve modeling results by the use of multivariate models which incorporate the wind direction, rotations per minute of the rotor, yaw, wind direction and pitch additional to the wind speed. The best improvement is achieved by the stochastic gradient boosted regression trees method for which the mean absolute error can be decreased by up to 27.66%. The third novelty consists of making a synthetic data set available for bench-marking purposes. Highlights: Data-driven non-parametric power curve models perform the best. Multivariate model outperforms univariate models. Data-driven multivariate models capture the phenomena in the data.