Structural performance degradation identification of offshore wind turbines based on variational mode decomposition with a Grey Wolf Optimizer algorithm. (15th July 2022)
- Record Type:
- Journal Article
- Title:
- Structural performance degradation identification of offshore wind turbines based on variational mode decomposition with a Grey Wolf Optimizer algorithm. (15th July 2022)
- Main Title:
- Structural performance degradation identification of offshore wind turbines based on variational mode decomposition with a Grey Wolf Optimizer algorithm
- Authors:
- Ji, Xiang
Tian, Zhe
Song, Hong
Liu, Fushun - Abstract:
- Abstract: The performance degradation assessment of offshore wind turbine (OWT) structures plays a crucial role in ensuring the safe operation of the structures. This paper presents a method for assessing the performance degradation of OWT structures based on an optimized variational mode decomposition (VMD) algorithm. The method introduces the technique of multisensor data fusion and the Grey Wolf Optimizer (GWO) algorithm to optimize the setting of VMD parameters and then enables the assessment of the structural performance degradation of offshore wind turbines by extracting the structural performance degradation signature of the structure from noise-reduction data. To demonstrate the correctness and advantages of the method in this paper, a 4-degree-of-freedom (4-DOF) system under the action of white noise is used. Numerical calculations show that the developed method can accurately identify the 5%, 10%, 15% or 20% reduction in the structural stiffness and the coefficient of variance of the optimal structure is decreased to 3.6% of the initial design. To further investigate the performance of the proposed strategy, physical tests of the offshore wind monopile structure are conducted. As the loss of the overall structural performance is simulated by removing different numbers of bolts from the connected device, it is demonstrated that the proposed method can effectively assess the structural performance. Finally, field measurements are carried out on a monopile OWT locatedAbstract: The performance degradation assessment of offshore wind turbine (OWT) structures plays a crucial role in ensuring the safe operation of the structures. This paper presents a method for assessing the performance degradation of OWT structures based on an optimized variational mode decomposition (VMD) algorithm. The method introduces the technique of multisensor data fusion and the Grey Wolf Optimizer (GWO) algorithm to optimize the setting of VMD parameters and then enables the assessment of the structural performance degradation of offshore wind turbines by extracting the structural performance degradation signature of the structure from noise-reduction data. To demonstrate the correctness and advantages of the method in this paper, a 4-degree-of-freedom (4-DOF) system under the action of white noise is used. Numerical calculations show that the developed method can accurately identify the 5%, 10%, 15% or 20% reduction in the structural stiffness and the coefficient of variance of the optimal structure is decreased to 3.6% of the initial design. To further investigate the performance of the proposed strategy, physical tests of the offshore wind monopile structure are conducted. As the loss of the overall structural performance is simulated by removing different numbers of bolts from the connected device, it is demonstrated that the proposed method can effectively assess the structural performance. Finally, field measurements are carried out on a monopile OWT located near Rudong County, Jiangsu Province, in the Yellow Sea of China. The impact of typhoons on the performance of the structure is effectively assessed by analyzing measurement data before and three days after the typhoon In-Fa through the wind farm, the potential application of the method in the structural safety assessment and analysis of OWTs is validated. Highlights: Optimization-assisted variational mode decomposition is proposed for noise reduction. Grey wolf optimization algorithm is applied to realize the autonomy in VMD process. Mahalanobis distance-based criterion is developed to evaluate structural degradation. Numerical and field tests have demonstrated the correctness of the proposed method. … (more)
- Is Part Of:
- Ocean engineering. Volume 256(2022)
- Journal:
- Ocean engineering
- Issue:
- Volume 256(2022)
- Issue Display:
- Volume 256, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 256
- Issue:
- 2022
- Issue Sort Value:
- 2022-0256-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-07-15
- Subjects:
- Offshore wind turbine -- Structural performance degradation -- Grey wolf optimizer -- Variational mode decomposition
Ocean engineering -- Periodicals
Ocean engineering
Periodicals
620.4162 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00298018 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.oceaneng.2022.111449 ↗
- Languages:
- English
- ISSNs:
- 0029-8018
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6231.280000
British Library DSC - BLDSS-3PM
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- 21587.xml