Power Curve Analytic for Wind Turbine Performance Monitoring and Prognostics

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 25, 2011
Onder Uluyol Girija Parthasarathy Wendy Foslien Kyusung Kim

Abstract

The manufacturer-provided power curve for a wind turbine indicates the expected power output for a given wind speed and air density. This work presents a performance analytic that uses the measured power and the power curve to compute a residual power. Because the power curve is not site-specific, the residual is masked by it and other external factors as well as by degradation in performance of worn or failing components. We delineate operational regimes and develop statistical condition indicators to adaptively trend turbine performance and isolate failing components. The approach is extended to include legacy wind turbines for which we may not have a manufacturer‘s power curve. In such cases, an empirical approach is used to establish a baseline for the power curve. The approach is demonstrated using supervisory control and data acquisition (SCADA) system data from two wind turbines owned by different operators.

How to Cite

Uluyol, O. ., Parthasarathy, G. ., Foslien, W. ., & Kim, K. . (2011). Power Curve Analytic for Wind Turbine Performance Monitoring and Prognostics. Annual Conference of the PHM Society, 3(1). https://doi.org/10.36001/phmconf.2011.v3i1.2078
Abstract 748 | PDF Downloads 1503

##plugins.themes.bootstrap3.article.details##

Keywords

condition based maintenance (CBM), Diagnostics and prognostics, Wind Turbine, power curve

References
Bell, M. B. & Foslien, W. K. (2005). Early event detection- results from a prototype implementation. In 17th Annual Ethylene Producers' Conference, Session TA006- Ethylene Plant Process Control. Spring National Meeting (pp. 727-741), Apr. 10-14, Atlanta, GA.

Caselitz, P., Giebhardt, J., Kruger, T. & Mevenkamp, M. (1996). Development of a fault detection system for wind energy converters. EUWEC’96 (pp1004–1007), May 2-–24, Goteborg, Sweden.

Gorinevsky, D., Dittmar, K., Mylaraswamy, D. & Nwadiogbu, E. (2002). Model-based diagnostics for an aircraft auxiliary power unit. IEEE Conference on Control Applications. (pp 215-220), Sept 18–20, Glasgow, Scotland.

International Electro-technical Commission (IEC) (2005): Wind turbines—Part 12-1: Power performance measurements of electricity producing wind turbines. IEC 61400-12-1, first edition.

Kim, K. & Mylaraswamy, D. (2006). Fault Diagnosis and Prognosis of Gas Turbine Engines Based on Qualitative Modeling. In ASME TurboExpo. (881–889), May 8–11, Barcelona, Spain.

Lekou, D.J., Mouzakis, F., Anastasopoulo, A. A. & Kourosis, D. (2009). Fused Acoustic Emission and Vibration Techniques for Health Monitoring of Wind Turbine Gearboxes and Bearings. In EWEC2009.

Milan, P. (2008). The stochastic power curve analysis of wind turbines. MS Thesis, University of Oldenburg, Germany.

Kusiak, A., & Li, W. (2011). The prediction and diagnosis of wind turbine faults. Renewable Energy 36, pp 16-23.

Osadciw, L. A., Yan, Y., Ye, X., Benson, G. & White, E., (2010) Wind Turbine Diagnostics based on Power Curve Using Particle Swarm Optimization. Book chapter in Wind Power Systems: Applications of Computational Intelligence, Wang, Lingfeng; Singh, Chanan; Kusiak, Andrew (Eds.) Springer.

Rareshide, E., Tindal, A., Johnson, C., Graves, A., Simpson, E., Bleeg, J., Harris, T., & Schoborg., D. (2009). Effects of complex wind regimes on turbine performance. Podium Presentation at the AWEA WINDPOWER Conference, May 4–7. Chicago, IL.

Tindal, A., Johnson, C., LeBlanc, M., Harman, K., Rareshide, E., & Graves, A. (2008). Site-specific adjustments to wind turbine power curves. Poster presentation at the AWEA WINDPOWER Conference, June 1–4, Houston, TX.

Wiggelinkhuizen, E., Verbruggen, T., Braam, H., Rademakers, L., Xiang, J. & Watson, S. (2008), Assessment of condition monitoring techniques for offshore wind farms, J. Sol. Energy Eng. 130 031004-1- 9, DOI: 10.1115/1.2931512.

Y. Yan, L. A. Osadciw, G. Benson, & E. White, (2009). Inverse data transformation for change detection in wind turbine diagnostics. Proceedings of 22nd IEEE Canadian Conference on Electrical and Computer Engineering (pp. 944–949), May 3–6, Delta St. John‘s, Newfoundland and Labrador, Canada.

Zaher, A.S. & McArthur, S.D.J. (2007) A Multi-agent fault detection system for wind turbine defect recognition and diagnosis. In Proceedings of Power Tech 2007, (pp. 22–27), July 1–5, Lausanne, Switz.

Zaher, A., McArthur, S.D.J. & Infield, D.G., (2009), Online wind turbine fault detection through automated SCADA data analysis, wind energy. Published online in Wiley Interscience DOI: 10.1002/we.319 (www.interscience. wiley.com).
Section
Technical Research Papers