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.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/wes-5-1375-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models
Simon Letzgus
CORRESPONDING AUTHOR
Machine Learning Group, Technische Universität Berlin, Straße
des 17. Juni 135, 10623 Berlin, Germany
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18 citations as recorded by crossref.
- Artificial intelligence based abnormal detection system and method for wind power equipment X. Ding et al. 10.1016/j.ijft.2024.100569
- A Normal Behavior Model Based on Power Curve and Stacked Regressions for Condition Monitoring of Wind Turbines F. Bilendo et al. 10.1109/TIM.2022.3196116
- On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data P. Dao 10.3390/en16052352
- Bridging Data and Diagnostics: A Systematic Review and Case Study on Integrating Trend Monitoring and Change Point Detection for Wind Turbines A. Al Hassan & P. Dao 10.3390/en18195166
- Detection of operating mode changes, without a priori model and in uncertain environments J. Ragot 10.1177/01423312221092527
- Graph machine learning for predicting wake interaction losses based on SCADA data F. Hammer et al. 10.1088/1742-6596/2505/1/012047
- RUL forecasting for wind turbine predictive maintenance based on deep learning S. Shah et al. 10.1016/j.heliyon.2024.e39268
- Use of Artificial Neural Networks and SCADA Data for Early Detection of Wind Turbine Gearbox Failures B. Puruncajas et al. 10.3390/machines13080746
- Cost-optimized probabilistic maintenance for condition monitoring of wind turbines with rare failures V. Begun & U. Schlickewei 10.1016/j.egyr.2024.10.041
- Multitarget Normal Behavior Model Based on Heterogeneous Stacked Regressions and Change-Point Detection for Wind Turbine Condition Monitoring F. Bilendo et al. 10.1109/TII.2023.3331766
- Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study S. Barber et al. 10.3390/en15155638
- Asset Management decision-making through data-driven Predictive Maintenance – an overview, techniques, benefits and challenges M. Krishna Menon & R. Tuladhar 10.21595/marc.2024.24232
- Azure machine learning studio and SCADA data for failure detection and prediction purposes: A case of wind turbine generator A. El-Menshawy et al. 10.1088/1757-899X/1201/1/012086
- Anomaly detection of wind turbines based on stationarity analysis of SCADA data P. Dao et al. 10.1016/j.renene.2024.121076
- Lightning Damage Detection Method Using Autoencoder: A Case Study on Wind Turbines with Different Blade Damage Patterns T. Matsui et al. 10.3390/wind5020012
- Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach P. Knes & P. Dao 10.3390/en17205055
- A Selective Review on Information Criteria in Multiple Change Point Detection Z. Gao et al. 10.3390/e26010050
- Combination of methods for change-point detection in operating of power generating equipment И. Казаков et al. 10.26102/2310-6018/2021.34.3.003
17 citations as recorded by crossref.
- Artificial intelligence based abnormal detection system and method for wind power equipment X. Ding et al. 10.1016/j.ijft.2024.100569
- A Normal Behavior Model Based on Power Curve and Stacked Regressions for Condition Monitoring of Wind Turbines F. Bilendo et al. 10.1109/TIM.2022.3196116
- On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data P. Dao 10.3390/en16052352
- Bridging Data and Diagnostics: A Systematic Review and Case Study on Integrating Trend Monitoring and Change Point Detection for Wind Turbines A. Al Hassan & P. Dao 10.3390/en18195166
- Detection of operating mode changes, without a priori model and in uncertain environments J. Ragot 10.1177/01423312221092527
- Graph machine learning for predicting wake interaction losses based on SCADA data F. Hammer et al. 10.1088/1742-6596/2505/1/012047
- RUL forecasting for wind turbine predictive maintenance based on deep learning S. Shah et al. 10.1016/j.heliyon.2024.e39268
- Use of Artificial Neural Networks and SCADA Data for Early Detection of Wind Turbine Gearbox Failures B. Puruncajas et al. 10.3390/machines13080746
- Cost-optimized probabilistic maintenance for condition monitoring of wind turbines with rare failures V. Begun & U. Schlickewei 10.1016/j.egyr.2024.10.041
- Multitarget Normal Behavior Model Based on Heterogeneous Stacked Regressions and Change-Point Detection for Wind Turbine Condition Monitoring F. Bilendo et al. 10.1109/TII.2023.3331766
- Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study S. Barber et al. 10.3390/en15155638
- Asset Management decision-making through data-driven Predictive Maintenance – an overview, techniques, benefits and challenges M. Krishna Menon & R. Tuladhar 10.21595/marc.2024.24232
- Azure machine learning studio and SCADA data for failure detection and prediction purposes: A case of wind turbine generator A. El-Menshawy et al. 10.1088/1757-899X/1201/1/012086
- Anomaly detection of wind turbines based on stationarity analysis of SCADA data P. Dao et al. 10.1016/j.renene.2024.121076
- Lightning Damage Detection Method Using Autoencoder: A Case Study on Wind Turbines with Different Blade Damage Patterns T. Matsui et al. 10.3390/wind5020012
- Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach P. Knes & P. Dao 10.3390/en17205055
- A Selective Review on Information Criteria in Multiple Change Point Detection Z. Gao et al. 10.3390/e26010050
1 citations as recorded by crossref.
Latest update: 06 Oct 2025
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.
One of the major challenges when working with wind turbine sensor data in practice is the...
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