  Modern wind turbines gather a wealth of data with Supervisory Control And
Data Acquisition (SCADA) systems. We study the short-term mutual dependencies
of a variety of observables by evaluating Pearson correlation matrices on a
moving time window. Using clustering on these matrices, we identify multiple
stable operational states, which characterize the non-stationarity of mutual
dependencies at a single turbine. They represent different turbine operational
settings.
  Moreover, we combine the clustering analysis with a construction of a
stochastic process to study the switching dynamics of those states in more
detail. Calculating the distances between correlation matrices we obtain a time
series that describes the behavior of the complex system in a collective way.
Assuming this time series to be governed by a Langevin equation, we estimate
the deterministic (drift) and stochastic (diffusion) components of the dynamics
to understand the underlying non-stationarity. After adapting our method to
specific features of our data, we are able to study the dynamics of operational
states and their transitions as well as to resolve hysteresis effects.
