A SOM based Anomaly Detection Method for Wind Turbines Health Management through SCADA Data



Published Nov 13, 2020
Mian Du Lina Bertling Tjernberg Shicong Ma Qing He Lin Cheng Jianbo Guo


In this paper, a data driven method for Wind Turbine system level anomaly detection and root sub-component identification is proposed. Supervisory control and data acquisition system (SCADA) data of WT is adopted and several parameters are selected based on physical knowledge in this domain and correlation coefficient analysis to build a normal behavior model. This model which is based on Self-organizing map (SOM) projects higher-dimensional SCADA data into a two-dimension-map. Afterwards, the Euclidean distance based indicator for system level anomalies is defined and a filter is created to screen out suspicious data points based on quantile function. Moreover, a failure data pattern based criterion is created for anomaly detection from system level. In order to track which sub-component should be responsible for an anomaly, a contribution proportion (CP) index is proposed. The method is tested with a two-month SCADA dataset with the measurement interval as 20 seconds. Results demonstrate capability and efficiency of the proposed method.

Abstract 166 | PDF Downloads 200



anomaly detection, Wind Turbine, SCADA, Critical Component, Self-organizing maps

Anderson, D. R., Burnham, K. P., & Thompson, W. L. (2000). Null hypothesis testing: problems, prevalence, and an alternative. The journal of wildlife management, 912-923.
Breteler, D., Kaidis, C., Tinga, T., & Loendersloot, R. (2015). Physics based methodology for wind turbine failure detection, diagnostics & prognostics. In EWEA 2015 (pp. 1-9). European Wind Energy Association.
Bruce, T., Long, H., & Dwyer-Joyce, R. S. (2015). Dynamic modelling of wind turbine gearbox bearing loading during transient events. Renewable Power Generation, IET, 9(7), 821-830.
Castellani, F., Astolfi, D., Sdringola, P., Proietti, S., & Terzi, L. (2015). Analyzing wind turbine directional behavior: SCADA data mining techniques for efficiency and power assessment. Applied Energy.
Chen, B. (2014). Automated On-line Fault Prognosis for Wind Turbine Monitoring using SCADA data. Durham University.
de Andrade Vieira, R., & Sanz-Bobi, M. (2013). Failure Risk Indicators for a Maintenance Model Based on Observable Life of Industrial Components With an Application to Wind Turbines. IEEE Transactions on Reliability, 62(3), 569 - 582.
de Azevedo, H. D., Araújo, A. M., & Bouchonneau, N. (2016). A review of wind turbine bearing condition monitoring: State of the art and challenges. Renewable and Sustainable Energy Reviews, 56, 368-379.
de Bessa, I. V., Palhares, R. M., D'Angelo, M. F., & Chaves Filho, J. E. (n.d.). Data-driven fault detection and isolation scheme for a wind turbine benchmark.
Depren, O., Topallar, M., Anarim, E., & Ciliz, M. K. (2005). An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks. Expert systems with Applications, 713-722.
European Wind Energy Association. (2016). Wind in power 2015 European statistics.
Fabio A, G., & Dipankar, D. (2003). Anomaly Detection Using Real-Valued Negative Selection. Genetic Programming and Evolvable Machines, 383-403.
Hardoon, D. R., Szedmak, S., & Shawe-Taylor, J. (2004). Canonical correlation analysis: An overview with application to learning methods. Neural computation, 16(12), 2639-2664.
Hoglund, A. J., Hatonen, K., & Sorvari, A. S. (2000). A computer host-based user anomaly detection system using the self-organizing map. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (pp. 411-416).
Kandukuri, S. T., Karimi, H. R., & Robbersmyr, K. G. (2016). A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management. Renewable and Sustainable Energy Reviews, 53, 697-708.
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological cybernetics, 59-69.
Lamedica, R., Prudenzi, A., Sforna, M., Caciotta, M., & Cencellli, V. O. (1996). A neural network based technique for short-term forecasting of anomalous load periods. IEEE Transactions on Power Systems, 1749-1756.
Lapira, Edzel, & al, e. (2012). Wind turbine performance assessment using multi-regime modeling approach. Renewable Energy(45), 86-95.
Marhadi, K. S., & Skrimpas, G. A. (2015). Automatic Threshold Setting and Its Uncertainty Quantification in Wind Turbine Condition Monitoring System. International Journal of Prognostics and Health Management,6(Special Issue Uncertainty in PHM).
Milborrow, D. (2006). Operation and maintenance costs compared and revealed. Windstats Newsletter, 19, 1-3.
Park, B., Sohn, H., Malinowski, P., & Ostachowicz, W. (2016). Delamination localization in wind turbine blades based on adaptive time-of-flight analysis of noncontact laser ultrasonic signals. Nondestructive Testing and Evaluation, 1-20.
Rigamonti, M. a., Zio, E., Alessi, A., Astigarraga, D., & Galarza, A. (2015). A Self-Organizing Map-Based Monitoring System for Insulated Gate Bipolar Transistors Operating in Fully Electric Vehicle. Annual Conference of the Prognostic and Health Management Society 2015.
Santos, P., Villa, L. F., Reñones, A., Bustillo, A., & Maudes, J. (2015). An SVM-based solution for fault detection in wind turbines. Sensors, 15(3), 5627-5648.
Schlechtingen, M., Santos, I. F., & Achiche, S. (2013). Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description. Applied Soft Computing, 13(1), 259-270.
Snchez-Silva, M. (2015). Degradation: Data Analysis and Analytical Modeling. In Reliability and Life-Cycle Analysis of Deteriorating System (pp. 79-82). Springer.
Sun, P., Li, J., Wang, C., & Lei, X. (2016). A generalized model for wind turbine anomaly identification based on SCADA data. Applied Energy, 168, 550-567.
Tian, J., Azarian, M. H., & Pecht, M. (2014). Anomaly Detection Using Self-Organizing Maps-Based K-Nearest Neighbor Algorithm. Proceedings of the European Conference of the Prognostics and Health Management Society. Citeseer.
Vesanto, J., Himberg, J., Alhoniemi, E., & Parhankangas, J. (2000). SOM toolbox for Matlab 5. Helsinki, Finland: Helsinki University of Technology.
Wilkinson, M., Darnell, B., Delft, T. V., & Harman, K. (2014). Comparison of methods for wind turbine condition monitoring with SCADA data. IET Renewable Power Generation, 390-397.
Xiang, J., Watson, S., & Liu, Y. (2009). Smart monitoring of wind turbines using neural networks. In Sustainability in Energy and Buildings (pp. 1-8). Springer.
Yang, D., Li, H., Hu, Y., Zhao, J., Xiao, H., & Lan, Y. (2016). Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion. Renewable Energy, 92, 104-116.
Zhao, W., Siegel, D., Lee, J., & Su, L. (2013). An integrated framework of drivetrain degradation assessment and fault localization for offshore wind turbines. IJPHM Special Issue on Wind Turbine PHM (Color), 46-58.
Zhong, B., Wang, J., Wu, H., Zhou, J., & Jin, Q. (2016). SOM-based visualization monitoring and fault diagnosis for chemical process. 2016 Chinese Control and Decision Conference (CCDC), (pp. 5844-5849).
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