LAPSE:2023.22868
Published Article

LAPSE:2023.22868
A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis
March 24, 2023
Abstract
Ageing of technical systems and machines is a matter of fact. It therefore does not come as a surprise that an energy conversion system such as a wind turbine, which in particular operates under non-stationary conditions, is subjected to performance decline with age. The present study presents an analysis of the performance deterioration with age of a Vestas V52 wind turbine, installed in 2005 at the Dundalk Institute of Technology campus in Ireland. The wind turbine has operated from October 2005 to October 2018 with its original gearbox, that has subsequently been replaced in 2019. Therefore, a key point of the present study is that operation data spanning over thirteen years have been analysed for estimating how the performance degrades in time. To this end, one of the most innovative approaches for wind turbine performance control and monitoring has been employed: a multivariate Support Vector Regression with Gaussian Kernel, whose target is the power output of the wind turbine. Once the model has been trained with a reference data set, the performance degradation is assessed by studying how the residuals between model estimates and measurements evolve. Furthermore, a power curve analysis through the binning method has been performed to estimate the Annual Energy Production variations and suggests that the most convenient strategy for the test case wind turbine (running the gearbox until its end of life) has indeed been adopted. Summarizing, the main results of the present study are as follows: over a ten-year period, the performance of the wind turbine has declined of the order of 5%; the performance deterioration seems to be nonlinear as years pass by; after the gearbox replacement, a fraction of performance deterioration has been recovered, though not all because the rest of the turbine system has been operating for thirteen years from its original state. Finally, it should be noted that the estimate of performance decline is basically consistent with the few results available in the literature.
Ageing of technical systems and machines is a matter of fact. It therefore does not come as a surprise that an energy conversion system such as a wind turbine, which in particular operates under non-stationary conditions, is subjected to performance decline with age. The present study presents an analysis of the performance deterioration with age of a Vestas V52 wind turbine, installed in 2005 at the Dundalk Institute of Technology campus in Ireland. The wind turbine has operated from October 2005 to October 2018 with its original gearbox, that has subsequently been replaced in 2019. Therefore, a key point of the present study is that operation data spanning over thirteen years have been analysed for estimating how the performance degrades in time. To this end, one of the most innovative approaches for wind turbine performance control and monitoring has been employed: a multivariate Support Vector Regression with Gaussian Kernel, whose target is the power output of the wind turbine. Once the model has been trained with a reference data set, the performance degradation is assessed by studying how the residuals between model estimates and measurements evolve. Furthermore, a power curve analysis through the binning method has been performed to estimate the Annual Energy Production variations and suggests that the most convenient strategy for the test case wind turbine (running the gearbox until its end of life) has indeed been adopted. Summarizing, the main results of the present study are as follows: over a ten-year period, the performance of the wind turbine has declined of the order of 5%; the performance deterioration seems to be nonlinear as years pass by; after the gearbox replacement, a fraction of performance deterioration has been recovered, though not all because the rest of the turbine system has been operating for thirteen years from its original state. Finally, it should be noted that the estimate of performance decline is basically consistent with the few results available in the literature.
Record ID
Keywords
machines ageing, performance monitoring, multivariate regression, power curve, useful lifetime, wind energy, wind turbines
Subject
Suggested Citation
Byrne R, Astolfi D, Castellani F, Hewitt NJ. A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis. (2023). LAPSE:2023.22868
Author Affiliations
Byrne R: Centre for Renewables and Energy, Dundalk Institute of Technology, Dublin Road, A91 V5XR Louth, Ireland
Astolfi D: Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Castellani F: Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy [ORCID]
Hewitt NJ: School of Architecture & the Built Environment, University of Ulster, Belfast BT9 5AG, UK
Astolfi D: Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Castellani F: Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy [ORCID]
Hewitt NJ: School of Architecture & the Built Environment, University of Ulster, Belfast BT9 5AG, UK
Journal Name
Energies
Volume
13
Issue
8
Article Number
E2086
Year
2020
Publication Date
2020-04-21
ISSN
1996-1073
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Original Submission
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PII: en13082086, Publication Type: Journal Article
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LAPSE:2023.22868
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https://doi.org/10.3390/en13082086
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Mar 24, 2023
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