Correlated power time series of individual wind turbines: A data driven model approach

Abstract

Wind farms can be regarded as complex systems that are on the one hand coupled to the nonlinear, stochastic characteristics of weather and on the other hand strongly influenced by supervisory control mechanisms. One crucial problem in this context today is the predictability of wind energy as an intermittent renewable resource with additional non-stationary nature. In this context, we analyse power time series measured in an offshore wind farm for a total period of one year with a time resolution of ten minutes. Applying Detrended Fluctuation Analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with crossover behaviour. To enrich the modelling perspective of complex large wind energy systems, we develop a stochastic reduced-form model of power time series. Observed transitions between two dominating power generation phases are reflected by a bistable deterministic component while correlated stochastic fluctuations account for the identified persistence. The model succeeds to qualitatively reproduce several empirical characteristics such as the autocorrelation function and the bimodal probability density function.