Preprints
https://doi.org/10.5194/wes-2021-107
https://doi.org/10.5194/wes-2021-107

  01 Oct 2021

01 Oct 2021

Review status: this preprint is currently under review for the journal WES.

Non-stationarity in correlation matrices for wind turbine SCADA-data and implications for failure detection

Henrik M. Bette, Edgar Jungblut, and Thomas Guhr Henrik M. Bette et al.
  • Faculty of Physics, University of Duisburg-Essen, Lotharstr. 1, Duisburg, Germany

Abstract. Modern utility-scale wind turbines are equipped with a Supervisory Control And Data Acquisition (SCADA) system gathering vast amounts of operational data that can be used for failure analysis and prediction to improve operation and maintenance of turbines. We analyse high freqeuency SCADA-data from the Thanet offshore windpark in the UK and evaluate Pearson correlation matrices for a variety of observables with a moving time window. This renders possible an asessment of non-stationarity in mutual dependcies of different types of data. Drawing from our experience in other complex systems, such as financial markets and traffic, we show this by employing a hierarchichal k-means clustering algorithm on the correlation matrices. The different clusters exhibit distinct typical correlation structures to which we refer as states. Looking first at only one and later at multiple turbines, the main dependence of these states is shown to be on wind speed. In accordance, we identify them as operational states arising from different settings in the turbine control system based on the available wind speed. We model the boundary wind speeds seperating the states based on the clustering solution. This allows the usage of our methodology for failure analysis or prediction by sorting new data based on wind speed and comparing it to the respective operational state, thereby taking the non-stationarity into account.

Henrik M. Bette et al.

Status: open (until 30 Nov 2021)

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Henrik M. Bette et al.

Henrik M. Bette et al.

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Short summary
We analyse the non-stationarity in Pearson correlation matrices for high frequency wind turbine data. Applying a hierarchichal k-means clustering to a time series of matrices, we distinguish different states, which exhibit distinct correlation structures. These arise from the turbine control system reacting to the current wind speed. We model boundary wind speeds between the different states. Our method enables accounting for the non-stationarity when predicting or analysing turbine failures.