Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models

Letzgus, Simon

doi:https://doi.org/10.5194/wes-5-1375-2020

Articles | Volume 5, issue 4

https://doi.org/10.5194/wes-5-1375-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Wind Energy Science Conference 2019

https://doi.org/10.5194/wes-5-1375-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 5, issue 4

Research article

|

27 Oct 2020

Research article |

| 27 Oct 2020

Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models

Simon Letzgus

Model code and software

Implementation of change-point detection algorithm and data samples Simon Letzgus https://doi.org/10.5281/zenodo.3728023

Short summary

One of the major challenges when working with wind turbine sensor data in practice is the presence of systematic changes in signal behaviour induced by malfunctions or maintenance actions. We found that approximately every third signal is affected by such change points and introduce an algorithm which reliably detects them in a highly automated fashion. The algorithm enables the application of data-driven techniques to monitor wind turbine components using data from commonly installed sensors.