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Structural health monitoring for a wind turbine system: a review of damage detection methods
Open accessAbstract
Renewable energy sources have gained much attention due to the recent energy crisis and the urge to get clean energy.Among the main options being studied, wind energy is a strong contender because of its reliability due to the maturity of the technology, good infrastructure and relative cost competitiveness.In order to harvest wind energy more efficiently, the size of wind turbines has become physically larger, making maintenance and repair works difficult.In order to improve safety considerations, to minimize down time, to lower the frequency of sudden breakdowns and associated huge maintenance and logistic costs and to provide reliable power generation, the wind turbines must be monitored from time to time to ensure that they are in good condition.Among all the monitoring systems, the structural health monitoring (SHM) system is of primary importance because it is the structure that provides the integrity of the system.SHM systems and the related non-destructive test and evaluation methods are discussed in this review.As many of the methods function on local damage, the types of damage that occur commonly in relation to wind turbines, as well as the damage hot spots, are also included in this review.
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Cited by
Martinez-Luengo, M., Kolios, A., & Wang, L. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.05.085 · Cited by 310
Structural Health Monitoring in Composite Structures: A Comprehensive Review
Hassani, S., Mousavi, M., & Gandomi, A. H. (2021) · Sensors · doi:10.3390/s22010153 · Cited by 258
Vitola, J., Pozo, F., Tibaduiza, D., & Anaya, M. (2017) · Sensors · doi:10.3390/s17061252 · Cited by 60
A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach
Bogoevska, S., Spiridonakos, M., Chatzi, E., Dumova-Jovanoska, E., & Höffer, R. (2017) · Sensors · doi:10.3390/s17040720 · Cited by 60
A Novel Structural Damage Identification Method Using a Hybrid Deep Learning Framework
He, Y., Huang, Z., Liu, D., Zhang, L., & Liu, Y. (2022) · Buildings · doi:10.3390/buildings12122130 · Cited by 16
Metadata
- Authors
- Ciang, C. C., Lee, J.-R., & Bang, H.-J.
- Venue
- Measurement Science and Technology
- Publisher
- IOP Publishing
- Published
- 2008-10-13
- Volume
- 19
- Issue
- 12
- Pages
- 122001
- ISSN
- 0957-0233 (ISSN-L)
- 1361-6501
- OpenAlex
- W1994533780
- Cited by
- 802
- References
- 0
- FWCI
- 55.17
- Type
- journal article
- Language
- en
- Open access
- bronze
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