English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In the last few years, the dynamical characterization of the power output ofa wind turbine by means of a Langevin equation has been well established. Forthis approach, temporally highly resolved measurements of wind speed and poweroutput are used to obtain the drift and diffusion coefficients of the energyconversion process. These coefficients fully determine a Langevin stochasticdifferential equation with Gaussian white noise. The drift term specifies thedeterministic behavior of the system whereas the diffusion term describes thestochastic behavior of the system. A precise estimation of these coefficientsis essential to understand the dynamics of the power conversion process of thewind turbine. We show that the dynamics of the power output of a wind turbinehave a hidden dependency on turbine's different operational states. Here, weuse an approach based on clustering Pearson correlation matrices for differentobservables on a moving time window to identify different operational states.We have identified five operational states in total, for example the state ofrated power. Those different operational states distinguish non-stationarybehavior in the mutual dependencies and represent different turbine controlsettings. As a next step, we condition our Langevin analysis on these differentstates to reveal distinctly different behaviors of the power conversion processfor each operational state. Moreover, in our new representation hysteresiseffects which have typically appeared in the Langevin dynamics of wind turbinesseem to be resolved. We assign these typically observed hysteresis effectsclearly to the change of the wind energy system between our estimated differentoperational states. In this contribution, we discuss further consequences forthe meaning of hysteric switching and detection of malbehaviors in windturbines.

Extension of the Langevin power curve analysis by separation per operational state