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.
Research article
| 
27 Oct 2020
Research article |
| 27 Oct 2020
| 27 Oct 2020
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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Total article views: 5,528 (including HTML, PDF, and XML)
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Total article views: 4,613 (including HTML, PDF, and XML)
Thereof 4,290 with geography defined
and 323 with unknown origin.
Total article views: 915 (including HTML, PDF, and XML)
Thereof 753 with geography defined
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Cited
23 citations as recorded by crossref.
- Artificial intelligence based abnormal detection system and method for wind power equipment X. Ding et al.
- A Normal Behavior Model Based on Power Curve and Stacked Regressions for Condition Monitoring of Wind Turbines F. Bilendo et al.
- 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
- Integrating Trend Monitoring and Change Point Detection for Wind Turbine Blade Diagnostics: A Physics-Driven Evaluation of Erosion and Twist Faults A. Hassan et al.
- Cost-optimized probabilistic maintenance for condition monitoring of wind turbines with rare failures V. Begun & U. Schlickewei
- Multitarget Normal Behavior Model Based on Heterogeneous Stacked Regressions and Change-Point Detection for Wind Turbine Condition Monitoring F. Bilendo et al.
- Anomaly detection of wind turbines based on stationarity analysis of SCADA data P. Dao et al.
- Lightning Damage Detection Method Using Autoencoder: A Case Study on Wind Turbines with Different Blade Damage Patterns T. Matsui et al.
- GMM-Based Lightning Damage Detection for Wind Turbines Under De-Rated Operation Using the Scaled Power Curve T. Matsui et al.
- A Selective Review on Information Criteria in Multiple Change Point Detection Z. Gao et al.
- Recent advances in wind turbine condition monitoring using SCADA data: A state-of-the-art review S. Wang et al.
- On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data P. Dao
- Detection of operating mode changes, without a priori model and in uncertain environments J. Ragot
- Graph machine learning for predicting wake interaction losses based on SCADA data F. Hammer et al.
- RUL forecasting for wind turbine predictive maintenance based on deep learning S. Shah et al.
- Use of Artificial Neural Networks and SCADA Data for Early Detection of Wind Turbine Gearbox Failures B. Puruncajas et al.
- 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.
- Asset Management decision-making through data-driven Predictive Maintenance – an overview, techniques, benefits and challenges M. Krishna Menon & R. Tuladhar
- A Cluster-Based Filtering Approach to SCADA Data Preprocessing for Wind Turbine Condition Monitoring and Fault Detection K. Kijanowski et al.
- Short-term wind power forecasting using KNN, SVR, and linear regression with high-resolution SCADA data S. Çelebi et al.
- Azure machine learning studio and SCADA data for failure detection and prediction purposes: A case of wind turbine generator A. El-Menshawy et al.
- Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach P. Knes & P. Dao
- An integrated change-point detection framework for wind turbine monitoring and fault diagnosis using SCADA data A. Hassan et al.
23 citations as recorded by crossref.
- Artificial intelligence based abnormal detection system and method for wind power equipment X. Ding et al.
- A Normal Behavior Model Based on Power Curve and Stacked Regressions for Condition Monitoring of Wind Turbines F. Bilendo et al.
- 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
- Integrating Trend Monitoring and Change Point Detection for Wind Turbine Blade Diagnostics: A Physics-Driven Evaluation of Erosion and Twist Faults A. Hassan et al.
- Cost-optimized probabilistic maintenance for condition monitoring of wind turbines with rare failures V. Begun & U. Schlickewei
- Multitarget Normal Behavior Model Based on Heterogeneous Stacked Regressions and Change-Point Detection for Wind Turbine Condition Monitoring F. Bilendo et al.
- Anomaly detection of wind turbines based on stationarity analysis of SCADA data P. Dao et al.
- Lightning Damage Detection Method Using Autoencoder: A Case Study on Wind Turbines with Different Blade Damage Patterns T. Matsui et al.
- GMM-Based Lightning Damage Detection for Wind Turbines Under De-Rated Operation Using the Scaled Power Curve T. Matsui et al.
- A Selective Review on Information Criteria in Multiple Change Point Detection Z. Gao et al.
- Recent advances in wind turbine condition monitoring using SCADA data: A state-of-the-art review S. Wang et al.
- On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data P. Dao
- Detection of operating mode changes, without a priori model and in uncertain environments J. Ragot
- Graph machine learning for predicting wake interaction losses based on SCADA data F. Hammer et al.
- RUL forecasting for wind turbine predictive maintenance based on deep learning S. Shah et al.
- Use of Artificial Neural Networks and SCADA Data for Early Detection of Wind Turbine Gearbox Failures B. Puruncajas et al.
- 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.
- Asset Management decision-making through data-driven Predictive Maintenance – an overview, techniques, benefits and challenges M. Krishna Menon & R. Tuladhar
- A Cluster-Based Filtering Approach to SCADA Data Preprocessing for Wind Turbine Condition Monitoring and Fault Detection K. Kijanowski et al.
- Short-term wind power forecasting using KNN, SVR, and linear regression with high-resolution SCADA data S. Çelebi et al.
- Azure machine learning studio and SCADA data for failure detection and prediction purposes: A case of wind turbine generator A. El-Menshawy et al.
- Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach P. Knes & P. Dao
- An integrated change-point detection framework for wind turbine monitoring and fault diagnosis using SCADA data A. Hassan et al.
Saved (final revised paper)
Latest update: 30 Apr 2026
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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