Integrating Structural Health Management with Contingency Control for Wind Turbines
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Published
Nov 1, 2020
Susan A. Frost
Kai Goebel
Léo Obrecht
Abstract
Maximizing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. In that context, systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage to the turbine. Advanced contingency control is one way to enable autonomous decision-making by providing the mechanism to enable safe and efficient turbine operation. The work reported herein explores the integration of condition monitoring of wind turbine blades with contingency control to balance the trade-offs between maintaining system health and energy capture. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine.
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Keywords
Wind Turbine, PHM, contingency control, blade damage
References
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Frost, S.A., Balas, M.J., & Wright, A.D. (June 2011), Generator speed regulation in the presence of structural modes through adaptive control using residual mode filters, Mechatronics, 21(4): 660-667.
Frost, S.A., Goebel, K., Trinh, K.V., Balas, M.J., & Frost, A.M. (Mar. 2011), Integrating Systems Health Management with Adaptive Controls for a Utility-scale Wind Turbine, Proceedings AIAA Infotech@Aerospace Conference, St. Louis, MO.
Jonkman, J.M. & Buhl, M.L. (Aug. 2005), FAST user’s guide, National Renewable Energy Laboratory, NREL/EL-500-38230, Golden, Colorado.
Laino, D.J. & Hansen, A.C. (Sept. 2001), User’s guide to the computer software routines AeroDyn interface for ADAMS®. Salt Lake City, Utah: Windward Engineering, LC.
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Wahl, N.K., Mandell, J.F., & Samborsky, D.D. (2001), Spectrum fatigue lifetime and residual strength for fiberglass laminates, Doctoral dissertation, Montana State University, Bozeman.
Wright, A.D., Balas, M.J. & Fingersh, L.J. (2006), Testing state-space controls for the controls advanced research turbine, Transactions of the ASME. Journal of Solar Energy Engineering; 128(4): 506-515.
Yang, J. N., Jones, D. L., Yang, S. H. & Meskini, A. (July 1990), A Stiffness Degradation Model for Graphite /Epoxy Laminates, Journal of Composite Materials, Vol. 24, pp. 753-769.
Zhang, Y.M. & Jiang, J. (June 9-11, 2003), Bibliographical Review on Reconfigurable Fault-Tolerant Control Systems, in Proc. of the 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Washington, D.C., USA, pp. 265-276.
Butterfield, A Sheng, S, & Oyague, F. (Sept. 9-11, 2009), Wind Energy’s New Role in Supplying the World’s Energy: What Role will Structural Health Monitoring Play?, 7th International Workshop on Structural Health Monitoring, Stanford, CA.
Doebling, S.W., Farrar, C.R., Prime, M.B. & Shevitz, D.W. (May 1996), Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review, Technical Report LA--13070-MS, Los Alamos National Lab., NM,.
Farrar, C.R. & Lieven (2007), N.A.J., Damage prognosis: the future of structural health monitoring, Phil. Trans. R. Soc. A, 365, pp. 623-632.
Fingersh, L.J. & Johnson, K.E. (Oct. 2002), Controls Advanced Research Turbine (CART) Commissioning and baseline data collection. National Renewable Energy Laboratory, NREL/TP-500-32879, Golden, CO.
Frost, S.A., Balas, M.J., Goebel, K, & Wright, A.D. (Jan. 2012), Adaptive contingency control: Wind turbine operation integrated with blade condition monitoring, Proceedings AIAA Aerospace Science Meeting, Wind Energy Symposium, Nashville, TN.
Frost, S.A., Balas, M.J., & Wright, A.D. (June 2011), Generator speed regulation in the presence of structural modes through adaptive control using residual mode filters, Mechatronics, 21(4): 660-667.
Frost, S.A., Goebel, K., Trinh, K.V., Balas, M.J., & Frost, A.M. (Mar. 2011), Integrating Systems Health Management with Adaptive Controls for a Utility-scale Wind Turbine, Proceedings AIAA Infotech@Aerospace Conference, St. Louis, MO.
Jonkman, J.M. & Buhl, M.L. (Aug. 2005), FAST user’s guide, National Renewable Energy Laboratory, NREL/EL-500-38230, Golden, Colorado.
Laino, D.J. & Hansen, A.C. (Sept. 2001), User’s guide to the computer software routines AeroDyn interface for ADAMS®. Salt Lake City, Utah: Windward Engineering, LC.
Litt, J.S., Parker, K.J., & Chatterjee, S. (2003), Adaptive Gas Turbine Engine Control for Deterioration Compensation due to Aging, Technical Report TM 2003-212607, NASA Glenn, Lewis, OH.
Lu, B., Li, Y., Wu, X., & Yang, Z. (June 24-26 2009), A review of recent advances in wind turbine condition monitoring and fault diagnosis, Proceedings IEEE Power Electronics and Machines in Wind Applications.
Manjock, A. (May 25, 2005), Evaluation report: Design codes FAST and ADAMS for load calculations of onshore wind turbines, Report No. 72042, Germanischer Lloyd WindEnergie GmbH, Hamburg, Germany.
Manwell, J.F., McGowan, J. G., & Rogers, A.L. (2009), Wind Energy Explained: Theory, Design and Application, 2nd ed., John Wiley & Sons Ltd.
Nelson, J.W., Cairns, D.S., & Riddle, T.W. (Jan. 2011), Manufacturing Defects Common to Composite Wind Turbine Blades: Effects of Defects, Proceedings AIAA Aerospace Science Meeting, Wind Energy Symposium, Orlando, FL.
NWTC Design Codes, FAST (2013) and TurbSim (2012), http://wind.nrel.gov/designcodes/simulators/fast/, http://wind.nrel.gov/designcodes/preprocessors/turbsim/ , National Renewable Energy Lab., Golden, CO.
Rumsey, M.A. & Paquette, J.A. (2008), Structural health monitoring of wind turbine blades, in Proc. SPIE, vol. 6933, Smart Sensor Phenomena, Technology, Networks, and Systems, p. 69330E.
Shore, D. & Bodson, M. (2005), Flight Testing of a Reconfigurable Control System on an Unmanned Aircraft, AIAA Journal of Guidance, Control, and Dynamics, vol. 28, no. 4, pp. 696-707.
Stol K.A. (Sept. 2004), Geometry and structural properties for the Controls Advanced Research Turbine (CART) from model tuning. National Renewable Energy Laboratory, NREL/SR-500-32087, Golden, Colorado.
Tang, L., Kacprzynski, G.J., Goebel, K., Saxena, A., Saha, B., & Vachtsevanos, G. (Oct. 6-9, 2008), Prognostics-enhanced Automated Contingency Management for Advanced Autonomous Systems, International Conference on Prognostics and Health Management, Denver, CO.
Wahl, N.K., Mandell, J.F., & Samborsky, D.D. (2001), Spectrum fatigue lifetime and residual strength for fiberglass laminates, Doctoral dissertation, Montana State University, Bozeman.
Wright, A.D., Balas, M.J. & Fingersh, L.J. (2006), Testing state-space controls for the controls advanced research turbine, Transactions of the ASME. Journal of Solar Energy Engineering; 128(4): 506-515.
Yang, J. N., Jones, D. L., Yang, S. H. & Meskini, A. (July 1990), A Stiffness Degradation Model for Graphite /Epoxy Laminates, Journal of Composite Materials, Vol. 24, pp. 753-769.
Zhang, Y.M. & Jiang, J. (June 9-11, 2003), Bibliographical Review on Reconfigurable Fault-Tolerant Control Systems, in Proc. of the 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Washington, D.C., USA, pp. 265-276.
Section
Technical Papers