Abstract
We are addressing two related applications for the analysis of data from wind turbines. First, we consider time series data arising from virtual sensors in numerical simulations as employed during product development, and, second, we investigate sensor data from condition monitoring systems of installed wind turbines. For each application we propose a data analysis procedure based on dimensionality reduction. In the case of virtual product development we develop tools to assist the engineer in the process of analyzing the time series data from large bundles of numerical simulations in regard to similarities or anomalies. For condition monitoring we develop a procedure which detects damages early in the sensor data stream.
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Notes
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European Wind Energy Association www.ewea.org/wind-energy-basics/faq.
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Acknowledgements
The authors would like to thank the German Federal Ministry of Education and Research (BMBF) for the opportunity to do research in the VAVID project under grant 01IS14005. We cordially thank Henning Lang and Tobias Tesch for their assistance with the numerical experiments.
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Garcke, J., Iza-Teran, R., Marks, M., Pathare, M., Schollbach, D., Stettner, M. (2017). Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines. In: Griebel, M., Schüller, A., Schweitzer, M. (eds) Scientific Computing and Algorithms in Industrial Simulations. Springer, Cham. https://doi.org/10.1007/978-3-319-62458-7_16
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DOI: https://doi.org/10.1007/978-3-319-62458-7_16
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