Multivariate Anomaly Detection and Early Warning Framework for Wind Turbine Condition Monitoring Using SCADA Data
Publication: Journal of Energy Engineering
Volume 149, Issue 6
Abstract
Wind speed power characteristics are essential in evaluating the state of the wind turbine. The supervisory control and data acquisition (SCADA) data are massively collected and could be important resources for condition monitoring and anomaly detection of wind turbines if properly utilized. A systematic early-stage anomaly detection framework is built in this work consisting of three phases: (1) an improved data cleaning algorithm based on kernel density estimation (KDE) is presented to remove outliers of SCADA data where the constraint of the Gaussian distribution assumption is eliminated for describing the real distribution of power outputs in each wind speed interval; (2) deep neural networks (DNNs) are used to establish a multivariate power curve (MPC) model where the dependencies of multidimensional variables on power output are considered and selected by Pearson correlation analysis; and (3) the sequential probability ratio test (SPRT) is adopted to estimate the distribution of power residuals and used for anomaly detection and early warning. The case studies verified the efficacy of the proposed framework where 91 faults from 38 wind turbines in two wind farms are successfully detected in the early stage.
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Data Availability Statement
Some data (including the SCADA data of the Wind turbine 13 in DN wind farm and the SCADA data of Wind turbine 33 in TY wind farm after processing), models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was partly supported by the National Natural Science Foundation of China (Grant No. 11802152) and Research and Development Project of Huadian Group (Grant No. CHDKJ-21-01-98).
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Information & Authors
Information
Published In
Journal of Energy Engineering
Volume 149 • Issue 6 • December 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 24, 2022
Accepted: Apr 2, 2023
Published online: Aug 25, 2023
Published in print: Dec 1, 2023
Discussion open until: Jan 25, 2024
ASCE Technical Topics:
- Case studies
- Data analysis
- Data collection
- Distribution functions
- Electric power
- Energy engineering
- Energy sources (by type)
- Engineering fundamentals
- Engines
- Equipment and machinery
- Mathematical functions
- Mathematics
- Methodology (by type)
- Power transmission
- Probability distribution
- Renewable energy
- Research methods (by type)
- Structural engineering
- Turbines
- Wind engineering
- Wind power
- Wind speed
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