Using Transfer Learning to Build Physics-Informed Machine Learning Models for Improved Wind Farm Monitoring

Laura Schröder, Nikolay Krasimirov Dimitrov, David Robert Verelst and John Aasted Sørensen
Additional contact information
Laura Schröder: DTU Wind Energy, Technical University of Denmark, 4000 Roskilde, Denmark
Nikolay Krasimirov Dimitrov: DTU Wind Energy, Technical University of Denmark, 4000 Roskilde, Denmark
David Robert Verelst: DTU Wind Energy, Technical University of Denmark, 4000 Roskilde, Denmark
John Aasted Sørensen: DTU Engineering Technology, Technical University of Denmark, 2750 Ballerup, Denmark

Energies, 2022, vol. 15, issue 2, 1-21

Abstract: This paper introduces a novel, transfer-learning-based approach to include physics into data-driven normal behavior monitoring models which are used for detecting turbine anomalies. For this purpose, a normal behavior model is pretrained on a large simulation database and is recalibrated on the available SCADA data via transfer learning. For two methods, a feed-forward artificial neural network (ANN) and an autoencoder, it is investigated under which conditions it can be helpful to include simulations into SCADA-based monitoring systems. The results show that when only one month of SCADA data is available, both the prediction accuracy as well as the prediction robustness of an ANN are significantly improved by adding physics constraints from a pretrained model. As the autoencoder reconstructs the power from itself, it is already able to accurately model the normal behavior power. Therefore, including simulations into the model does not improve its prediction performance and robustness significantly. The validation of the physics-informed ANN on one month of raw SCADA data shows that it is able to successfully detect a recorded blade angle anomaly with an improved precision due to fewer false positives compared to its purely SCADA data-based counterpart.

Keywords: transfer learning; informed machine learning; performance monitoring; simulation-based neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/2/558/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/2/558/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:2:p:558-:d:723940

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().