Analysis of two modeling approaches for fatigue estimation and remaining useful life predictions of wind turbine blades
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Abstract
Wind turbines components are subject to considerable stresses and fatigue due to extreme environmental conditions to which they are exposed, especially those located offshore. With this
aim, the present work explores two different approaches on fatigue damage estimation and remaining useful life predictions of wind turbine blades. The first approach uses the rainflow counting algorithm. The second approach comes from a fatigue damage model that describes the propagation of damage at a microscopic scale due to matrix cracks which manifests in a macroscopic scale as stiffness loss. Both techniques have been tested using the information provided by the blade root moment sensor signal obtained from the well known wind turbine simulator FAST (Fatigue, Aerodynamics, Structures and Turbulence).
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PHM
Bendat, J. (1964). Probability functions for random responses (Technical Report on contract NAS-5-4590). NASA (National Aeronautics and Space Administration).
Brondsted, P., & Nijssen, R. (2013). Advances in wind turbine blade design and materials. Woodhead Publishing.
Burton, T., Jenkins, N., Sharpe, D., & Bossanyi, E. (2011). Wind energy handbook [Computer software manual].
Caprile, C., Sala, G., & Buzzi, A. (1995). Environmental and mechanical fatigue of wind turbine blades made of composites materials. Journal of Reinforced Plastics and Composites(15), 673–691.
Dirlik, T. (1985). Application of computers in fatigue analysis (PhD. Thesis). Warwick University.
Downing, S., & Socie, D. (1982). Simple rainflow counting algorithms. International Journal of Fatigue, 4(1), 31–40.
Eliopoulos, E., & Philippidis, T. (2011). A progressive damage simulation algorithm for GFRP composites under cyclic loading. part i: Material constitutive model. Composites Science and Technology, 71(5), 742–749.
Endo, T., Mitsunaga, K., & Nakagawa, H. (1967). Fatigue of metals subjected to varying stress-prediction of fatigue lives. In Preliminary proceedings of the chugokushikoku district meeting (pp. 41–44).
Frost, S., Goebel, K., & Obrecht, L. (2013). Integrating structural health management with contingency control for wind turbines. International Journal of Prognostics and Health Management, 4(9), 11–20.
Hammerum, K., Brath, P., & Poulsen, N. (2007). A fatigue approach to wind turbine control. In Journal of physics: Conference series (Vol. 75, pp. 012–081).
Iung, B., Monnin, M., Voisin, P., & Cocheteux, E. (2008). Degradation state model-based prognosis for proactively maintaining product performance. CIRP Annals - Manufacturing Technology, 57(1), 49–52.
Jelavic, M., Petrovic, V., & Peric, N. (2008, Nov). Individual pitch control of wind turbine based on loads estimation. In Industrial electronics, 2008. iecon 2008. 34th annual conference of ieee (p. 228-234).
Jonkman, J., & Marshall, L. (2005). Fast user’s guide [Computer software manual].
Kensche, C., & Seifert, H. (1990). Wind turbine rotor blades under fatigue loads. In 4th. european conference composite materials (pp. 173–180).
Lee, Y., Pan, J., Hathaway, R., & Barkey, M. (2005). Fatigue testing and analysis: theory and practice (Vol. 13). Butterworth-Heinemann.
Martinen, S., Carlé
n, I., Nilsson, K., Breton, S.-P., & Ivanell, S. (2014). Analysis of the effect of curtailment on power and fatigue loads of two aligned wind turbines using an actuator disc approach. Journal of Physics: Conference Series, 524(1), 012182.
Myrent, N., Kusnick, J., & Adams, D. (2013). Pitch error and shear web disbond detection on wind turbine blades for offshore structural health and prognostics management. In 54th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference. Boston, United States of America.
Niesłony, A. (2009). Determination of fragments of multiaxial service loading strongly influencing the fatigue of machine components. Mechanical Systems and Signal Processing, 23(8), 2712–2721.
Nijssen, R. (2006). Fatigue life prediction and strength degradation of wind turbine rotor blade composites (PhD. Thesis).
Rychlik, I. (1987). A new definition of the rainflow cycle counting method. International journal of fatigue, 9(2), 119–121.
Sanchez, H., Escobet, T., Puig, V., & Odgaard, P. (2015). Health-aware model predictive control of wind turbines using fatigue prognosis. In 9th IFAC safeprocess (pp. 1363–1368). Paris, France.
Saxena, A., Celaya, J., Saha, B., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of the PHM Society, 1(20).
Schulte, K. (1985). Stiffness reduction and development of longitudinal cracks during fatigue loading of composite laminates. Mechanical characterisation of load bearing fibre composite laminates, 36–54.
Sutherland, H. (1999). On the fatigue analysis of wind turbines (No. SAND99-0089). Albuquerque, New Mexico. Van Paepegem, W., & Degrieck, J. (2002). A new coupled approach of residual stiffness and strength for fatigue of fibre-reinforced composites. International Journal of Fatigue, 24(7), 747–762.
Vassilopoulos, A. (2013). Fatigue life prediction of wind turbine blade composite materials. In P. B. ndsted & R. Nijssen (Eds.), Advances in wind turbine blade design and materials (pp. 251–297). Woodhead Publishing.
Vassilopoulos, A., & Nijssen, R. (2010). Fatigue life prediction of composite materials under realistic loading conditions (variable amplitude loading). In A. Vassilopoulos (Ed.), Fatigue life prediction of composites and composite structures (pp. 293–333). Woodhead Publishing Limited, Cambridge.
Zhang, Y., A.P., V., & Keller, T. (2008). Stiffness degradation and fatigue life prediction of adhesively-bonded joints for fi ber-reinforced polymer composites. International Journal of Fatigue, 30(10), 1813–1820.
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