Data-driven models for strain-based damage identification in composite wind turbine blades

Data-driven models for strain-based damage identification in composite wind turbine blades

Julián Sierra, Juan C. Perafan, Camilo Herrera, César Nieto

Abstract. The increasing demand for renewable energy has led to the development of several wind energy projects. A rising concern is the aging of these structures that must keep their serviceability and integrity for a long lifetime. That is why recent studies have focused on monitoring systems for data extraction based on accelerometers, fiber optic sensors, and piezoelectric sensors among many other sensing technologies. One of the most promising approaches is the use of fiber-Bragg-grating-based systems taking advantage of their proven benefits such as electromagnetic immunity, low size and weight, and ability to embed numerous sensors in a single optical fiber line. However, most of the reported studies have addressed the operational assessment of the acquisition systems without further deepening the exploitation of the acquired data for structural health monitoring purposes. This work aims to data exploitation of strain measurements acquired by simulated fiber Bragg gratings (FBG) for damage identification in wind turbine blades made of composite materials. A FEM model of a 2.5-meter-long wind turbine blade with 40 virtual FBGs strain sensors was used to obtain strain data under normal operational conditions. Then, strain measurements were calculated after defining several damages to the blade. Once the data were obtained, different data processing techniques following the pattern recognition paradigm were tested comparing their performance in terms of accuracy. The results will contribute to designing real-time automatic damage identification systems using FBGs strain sensors for composite wind turbine blades.

Keywords
Wind Energy, Damage Detection, Pattern Recognition, Fiber Bragg Gratings

Published online 3/30/2023, 8 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Julián Sierra, Juan C. Perafan, Camilo Herrera, César Nieto, Data-driven models for strain-based damage identification in composite wind turbine blades, Materials Research Proceedings, Vol. 27, pp 17-24, 2023

DOI: https://doi.org/10.21741/9781644902455-3

The article was published as article 3 of the book Structural Health Monitoring

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

References
[1] P. Kaewniam, M. Cao, N. F. Alkayem, D. Li und E. Manoach, „Recent advances in damage detection of wind turbine blades: A state-of-the-art review,“ Renewable and Sustainable Energy Reviews, Bd. 167, p. 112723, 2022. https://doi.org/10.1016/j.rser.2022.112723
[2] Y. Du, S. Zhou, X. Jing, Y. Peng, H. Wu und N. Kwok, „Damage detection techniques for wind turbine blades: A review,“ Mechanical Systems and Signal Processing, Bd. 141, p. 106445, 2020. https://doi.org/10.1016/j.ymssp.2019.106445
[3] J. Sierra-Pérez, M. A. Torres-Arredondo und A. Güemes, „Damage and nonlinearities detection in wind turbine blades based on strain field pattern recognition. FBGs, OBR and strain gauges comparison,“ Composite Structures, Bd. 135, pp. 156-166, 2016. https://doi.org/10.1016/j.compstruct.2015.08.137
[4] R. E. Murray, R. Beach, D. Barnes, D. Snowberg, D. Berry, S. Rooney, M. Jenks, B. Gage, T. Boro, S. Wallen und S. Hughes, „Structural validation of a thermoplastic composite wind turbine blade with comparison to a thermoset composite blade,“ Renewable Energy, Bd. 164, pp. 1100-1107, 2021. https://doi.org/10.1016/j.renene.2020.10.040
[5] J. Ye, C. Chu, H. Cai, X. Hou, B. Shi, S. Tian, X. Chen und J. Ye, „A multi-scale model for studying failure mechanisms of composite wind turbine blades,“ Composite Structures, Bd. 212, pp. 220-229, 2019. https://doi.org/10.1016/j.compstruct.2019.01.031
[6] K. Chandrasekhar, N. Stevanovic, E. J. Cross, N. Dervilis und K. Worden, „Damage detection in operational wind turbine blades using a new approach based on machine learning,“ Renewable Energy, Bd. 168, pp. 1249-1264, 2021. https://doi.org/10.1016/j.renene.2020.12.119