Dynamics of wind turbine operational states

TL;DR

This paper analyzes wind turbine data to identify stable operational states and their switching dynamics using correlation matrices, clustering, and stochastic modeling, revealing non-stationary behavior and hysteresis effects.

Contribution

It introduces a novel approach combining correlation-based clustering and stochastic process modeling to study turbine operational state dynamics.

Findings

Identification of multiple stable operational states.

Detection of non-stationary switching behavior.

Observation of hysteresis effects in state transitions.

Abstract

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.