Version 1
: Received: 17 January 2021 / Approved: 18 January 2021 / Online: 18 January 2021 (15:12:17 CET)
How to cite:
Rashid, H.; Batunlu, C. Anomaly Detection of Wind Turbine Gearbox based on SCADA
Temperature Data using Machine Learning. Preprints2021, 2021010356. https://doi.org/10.20944/preprints202101.0356.v1
Rashid, H.; Batunlu, C. Anomaly Detection of Wind Turbine Gearbox based on SCADA
Temperature Data using Machine Learning. Preprints 2021, 2021010356. https://doi.org/10.20944/preprints202101.0356.v1
Rashid, H.; Batunlu, C. Anomaly Detection of Wind Turbine Gearbox based on SCADA
Temperature Data using Machine Learning. Preprints2021, 2021010356. https://doi.org/10.20944/preprints202101.0356.v1
APA Style
Rashid, H., & Batunlu, C. (2021). Anomaly Detection of Wind Turbine Gearbox based on SCADA
Temperature Data using Machine Learning. Preprints. https://doi.org/10.20944/preprints202101.0356.v1
Chicago/Turabian Style
Rashid, H. and Canras Batunlu. 2021 "Anomaly Detection of Wind Turbine Gearbox based on SCADA
Temperature Data using Machine Learning" Preprints. https://doi.org/10.20944/preprints202101.0356.v1
Abstract
Wind energy is becoming an essential source of power for countries which have the aim to reduce greenhouse gases emission and mitigate the effects of global warming. The Wind Turbines (WTs) installed around the globe is increasing significantly every year. The dramatic increase in wind power has encountered quite a few challenges, among which the major issues are availability and reliability. The unexpected failure in WTs Gearbox (GB) ultimately increases the Operation and Maintenance (O&M) cost. The identification of faults in the earlier stages before it turns to catastrophic damage to other components of WT is crucial. This research deals with the prediction of WT failures by using a Supervisory Control and Data Acquisition (SCADA) system. The main aim is to forecast the temperature of the WTs GB to predict the impending overheating of the GB at an early stage. The earlier prediction will help to optimize the maintenance period and to save maintenance expenses and, even more important, generate warnings in due time to avoid major damages or even technical disasters. In the proposed method we compared six different machine learning (ML) models based on error and accuracy of prediction. The bagging regressor is the best ML model, which results in the mean square error of 0.33 and R of 99.8 on training data. The bagging regressor is then used to predict the fault in the WT GB, which detected the anomalous behavior of WT GB 59 days earlier than the actual failure. This model also detects the extremely unusual behavior of the GB 9 days earlier than a complete failure.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
