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

Braun, T., Waechter, M., Peinke, J., Guhr, T. (2020): Correlated power time series of individual wind turbines: A data driven model approach. - Journal of Renewable and Sustainable Energy, 12, 2, 023301.
https://doi.org/10.1063/1.5139039

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 Creators:
Braun, Tobias1, Author              
Waechter, M.2, Author
Peinke, J.2, Author
Guhr, T.2, Author
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1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 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 analyze the power time series measured in an offshore wind farm for a total period of one year with a time resolution of 10 min. Applying detrended fluctuation analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with crossover behavior. To enrich the modeling perspective of complex large wind energy systems, we develop a stochastic reduced-form model of power time series. The 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.

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 Dates: 2020
 Publication Status: Finally published
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 Rev. Type: Peer
 Identifiers: DOI: 10.1063/1.5139039
PIKDOMAIN: RD4 - Complexity Science
eDoc: 8959
Working Group: Development of advanced time series analysis techniques
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Title: Journal of Renewable and Sustainable Energy
Source Genre: Journal, SCI, p3
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Pages: - Volume / Issue: 12 (2) Sequence Number: 023301 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals301
Publisher: American Institute of Physics (AIP)