Skip to main content

Research Repository

Advanced Search

Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines

Chatterjee, Joyjit; Dethlefs, Nina

Authors

Joyjit Chatterjee



Abstract

The last decade has witnessed an increased interest in applying machine learning techniques to predict faults and anomalies in the operation of wind turbines. These efforts have lately been dominated by deep learning techniques which, as in other fields, tend to outperform traditional machine learning algorithms given sufficient amounts of training data. An important shortcoming of deep learning models is their lack of transparency—they operate as black boxes and typically do not provide rationales for their predictions, which can lead to a lack of trust in predicted outputs. In this article, a novel hybrid model for anomaly prediction in wind farms is proposed, which combines a recurrent neural network approach for accurate classification with an XGBoost decision tree classifier for transparent outputs. Experiments with an offshore wind turbine show that our model achieves a classification accuracy of up to 97%. The model is further able to generate detailed feature importance analyses for any detected anomalies, identifying exactly those components in a wind turbine that contribute to an anomaly. Finally, the feasibility of transfer learning is demonstrated for the wind domain by porting our “offshore” model to an unseen dataset from an onshore wind farm. The latter model achieves an accuracy of 65% and is able to detect 85% of anomalies in the unseen domain. These results are encouraging for application to wind farms for which no training data are available, for example, because they have not been in operation for long.

Citation

Chatterjee, J., & Dethlefs, N. (2020). Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines. Wind energy, 23(8), 1693-1710. https://doi.org/10.1002/we.2510

Journal Article Type Article
Acceptance Date Mar 17, 2020
Online Publication Date Apr 13, 2020
Publication Date 2020-08
Deposit Date Feb 1, 2021
Journal Wind Energy
Print ISSN 1095-4244
Electronic ISSN 1099-1824
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 23
Issue 8
Pages 1693-1710
DOI https://doi.org/10.1002/we.2510
Keywords LSTM; SCADA; SMOTE; Transfer learning; XGBoost
Public URL https://hull-repository.worktribe.com/output/3503302
Publisher URL https://onlinelibrary.wiley.com/doi/abs/10.1002/we.2510