Classification and Detection of Wind Turbine Pitch Faults Through SCADA Data Analysis

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Published Nov 1, 2020
Jamie L. Godwin Peter Matthews

Abstract

The development of electrical control system faults can lead to increased mechanical component degradation, severe reduction of asset performance, and a direct increase in annual maintenance costs. This paper presents a highly accurate data driven classification system for the diagnosis of electrical control system faults, in particular, wind turbine pitch faults. Early diagnosis of these faults can enable operators to move from traditional corrective or time based maintenance policy towards a predictive maintenance strategy, whilst simultaneously mitigating risks and requiring no further capital expenditure. Our approach provides transparent, human-readable rules for maintenance operators which have been validated by an independent domain expert. Data from 8 wind turbines was collected every 10 minutes over a period of 28 months with 10 attributes utilised to diagnose pitch faults. Three fault classes are identified: “no pitch fault”, “potential pitch fault” and “pitch fault established”. Of the turbines, 4 are used to train the system with a further 4 for validation. Repeated random sub-sampling of the majority fault class was used to reduce computational overheads whilst retaining information content and balancing the training and validation sets. A classification accuracy of 85.50% was achieved with 14 human readable rules generated via the RIPPER inductive rule learner. Of these rules, 11 were described as “useful and intuitive” by an independent domain-expert. An expert system was developed utilising the model along with domain knowledge, resulting in a pitch fault diagnostic accuracy of 87.05% along with a 42.12% reduction in pitch fault alarms.

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Keywords

Wind Turbine, data mining, SCADA, pitch faults, data analysis

References
Alpaydin, E. (2004). Introduction to Machine Learning. Cambridge, MA: The MIT Press.
Bianchi, F., De Battista, H., & Mantz, R.J. (2006). WT Control Systems – Principles, Modelling and Gain Scheduling Design. London: Springer.
Chen B., Qiu Y., Feng Y., Tavner P., & Song W. (2011). Wind turbine scada alarm pattern recognition. Renewable Power Generation (1-6). 6-8 September, Edinburgh, UK.
Cohen, W. W. (1995). Fast effective rule induction. International conference on Machine Learning (115-123). 9-12 July, California, USA.
Cohen W. & Singer Y., (1999). A simple, fast, and effective rule learner,” National. Conference on Artificial Intelligence. (335 – 342). 18-22 July, Florida, USA.
Crabtree C.J. (2010). Survey of commercially available condition monitoring systems for wind turbines. Durham University, 2010.
Djurdjanovic D., Lee J., & Ni J., (2003). Watchdog agent an infotronics-based prognostics approach for product performance degradation assessment and prediction. Advanced Engineering Informatics. 17(3), pp. 109–125.
Eti M., Ogaji S., & Probert S. (2006). Reducing the cost of preventive maintenance (pm) through adopting a proactive reliability-focused culture. Applied Energy 83(11) pp. 1235-1248.
Garcia V., Sanchez J., Martin-Felez R., & Mollineda R., (2012). Surrounding neighborhood-based smote for learning from imbalanced data sets. Progress in Artificial Intelligence, 1(4) pp. 1–16.
Gomez Fernandez J. & Crespo Marquez A. (2009). Framework for implementation of maintenance management in distribution network service providers. Reliability Engineering & System Safety 94(10) pp.1639-1649.
Hameed Z., Hong Y., Cho Y., Ahn S., & Song C. (2009). Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable energy reviews, 13(1) pp. 1–39.
Hatch C. (2004). Improved wind turbine condition monitoring using acceleration enveloping. GE Energy Journal of Electrical Systems, 3(1) pp. 26-38.
Jardine A., Lin D., & Banjevic D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7) pp. 1483–1510.
Kim K., Parthasarathy G., Uluyol O., Foslien W., Sheng S., & Fleming P. (2011). Use of SCADA Data for Failure Detection in Wind Turbines. Energy Sustainability and Fuel Cell Science. ESFuelCell2011-54243. 7-10 August, Washington DC, USA.
Kontogiannis, T., & Kossiavelou, Z. (1999). Stress and team performance: principles and challenges for intelligent decision aids, Safety Science. 33(3) pp. 103-128.
Kusiak A & Li W. (2011).The prediction and diagnosis of wind turbine faults.Renewable Energy.36(1)pp.16-23.
Kusiak A. & Verma A. (2011). A data-driven approach for monitoring blade pitch faults in wind turbines. Sustainable Energy, IEEE Trans. on, 2(1) pp. 87–96.
Levrat E., Iung B., & Marquez A. (2008). E-maintenance: review and conceptual framework. Production Planning and Control, 19(4) pp. 408–429.
Lin J. & Zuo M. (2003). Gearbox fault diagnosis using adaptive wavelet filter. Mechanical Systems and Signal Processing, 17(6) pp. 1259–1269.
Marais K. & Saleh J. (2009). Beyond its cost, the “value” of maintenance: An analytical framework for capturing its net present value. Reliability Engineering & System Safety, 94(2) pp. 644 - 657.
Massoud Amin S. & Wollenberg B. (2005). Toward a smart grid: power delivery for the 21st century. Power and Energy Magazine, IEEE, 3(5), pp. 34–41.
Matthews B. (1975). Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochemica et Biophysica Acta, 405(2) pp. 442-451.
Moore W. & Starr A. (2006). An intelligent maintenance system for continuous cost-based prioritisation of maintenance activities. Computers in Industry, 57(6) pp. 595–606.
Niu G., Yang B., & Pecht M., (2010). Development of an optimized condition-based maintenance system by data fusion and reliability centered maintenance. Reliability Engineering & System Safety, 95(7) pp. 786–796.
Quinlan, J. R. (1990). Learning logical definitions from relations. Machine Learning, 5(3), pp. 239-266.
Rafiee J., Rafiee M., & Tse P. (2010). Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications, 37(6) pp. 4568–4579.
Sainz E,, Llombart A., & Guerrero J. (2009). “Robust filtering for the characterization of wind turbines: Improving its operation and maintenance. Energy Conversion and Management, 50(9) pp. 2136–2147.
Wang X. & Makis V. (2009). Autoregressive model-based gear shaft fault diagnosis using the kolmogorov–smirnov test. Journal of Sound and Vibration, 327(3), pp.413–423.
Wu S. & Clements-Croome D. (2005). Preventive maintenance models with random maintenance quality. Reliability Engineering & System Safety. 90(1) pp. 99-105.
WWEA (2012). “Quarterly bulletin,” World Wind Energy Association Bulletin, 3(1), October, pp. 1 – 40.
Zaher, S. McArthur, D. Infield, & Y. Patel. (2009). Online wind turbine fault detection through automated scada data analysis. Wind Energy, 12(6) pp.574–593.
Section
Technical Papers