Dresden 2026 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 63: Nonlinear Stochastic Systems

DY 63.4: Talk

Friday, March 13, 2026, 12:15–12:30, ZEU/0118

Estimation of typical time scales in a Langevin-type model for wind turbine power conversion — •Martin Wagner and Joachim Peinke — Carl von Ossietzky Universität Oldenburg, School of Mathematics and Science, Institute of Physics, ForWind - Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany

In the recent years, a data-driven and computationally efficient Langevin approach has been successfully used to detect physically reasonable fixed points in the short-term power conversion dynamics of wind turbines [1]. In our contribution, we aim to use this model to describe the multi-body interaction and possible synergy effects of the power production of many turbines in a wind farm. Additionally to the fixed points, this requires the extraction of the typical inertial time scales of the power conversion from the stochastic model. We find that this is difficult, since the model does not fulfil the Markov property due to a projection that induces a memory kernel to the stochastic differential equation according to the Mori-Zwanzig formalism [2]. Nevertheless, we show that upper boundary estimates for the typical inertial time scales of a wind turbine can be extracted from the stochastic model. In the future, this is a first step towards the stochastic modelling of many turbines, e.g. to model a grid-supportive power production of a whole wind farm.

[1] Milan P, et al. Phys Rev Lett. 2013;110(13):138701.

[2] Zwanzig R. Nonequilibrium Statistical Mechanics. Oxford University Press; 2001. p. 149 ff.f.

Keywords: Stochastic Differential Equations; Markov Process; Correlated Noise; Mori-Zwanzig Formalism; Wind Turbine Power Conversion