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Feasibility of a fully autonomous wireless monitoring system for a wind turbine blade

Author

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  • Esu, O.O.
  • Lloyd, S.D.
  • Flint, J.A.
  • Watson, S.J.

Abstract

Condition monitoring (CM) of wind turbine blades has significant benefits for wind farm operators and insurers alike. Blades present a particular challenge in terms of operations and maintenance: the wide range of materials used in their construction makes it difficult to predict lifetimes; loading is stochastic and highly variable; and access can be problematic due to the remote locations where turbines are frequently located, particularly for offshore installations. Whilst previous works have indicated that Micro Electromechanical Systems (MEMS) accelerometers are viable devices for measuring the vibrations from which diagnostic information can be derived, thus far there has been no analysis of how such a system would be powered. This paper considers the power requirement of a self-powered blade-tip autonomous system and how those requirements can be met. The radio link budget is derived for the system and the average power requirement assessed. Following this, energy harvesting methods such as photovoltaics, vibration, thermal and radio frequency (RF) are explored. Energy storage techniques and energy regulation for the autonomous system are assessed along with their relative merits. It is concluded that vibration (piezoelectric) energy harvesting combined with lithium-ion batteries are suitable selections for such a system.

Suggested Citation

  • Esu, O.O. & Lloyd, S.D. & Flint, J.A. & Watson, S.J., 2016. "Feasibility of a fully autonomous wireless monitoring system for a wind turbine blade," Renewable Energy, Elsevier, vol. 97(C), pages 89-96.
    • Handle: RePEc:eee:renene:v:97:y:2016:i:c:p:89-96
    DOI: 10.1016/j.renene.2016.05.021
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    References listed on IDEAS

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    1. Wang, Jian-Xu & Su, Wen-Bin & Li, Ji-Chao & Wang, Chun-Ming, 2022. "A rotational piezoelectric energy harvester based on trapezoid beam: Simulation and experiment," Renewable Energy, Elsevier, vol. 184(C), pages 619-626.
    2. Cheng, Tinghai & Fu, Xianpeng & Liu, Wenbo & Lu, Xiaohui & Chen, Xiyan & Wang, Yingting & Bao, Gang, 2019. "Airfoil-based cantilevered polyvinylidene fluoride layer generator for translating amplified air-flow energy," Renewable Energy, Elsevier, vol. 135(C), pages 399-407.
    3. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    4. Zhou, H.F. & Zheng, J.F. & Xie, Z.L. & Lu, L.J. & Ni, Y.Q. & Ko, J.M., 2017. "Temperature effects on vision measurement system in long-term continuous monitoring of displacement," Renewable Energy, Elsevier, vol. 114(PB), pages 968-983.

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