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Condition monitoring benefit for onshore wind turbines: sensitivity to operational parameters
Abstract
The economic case for condition monitoring (CM) applied to wind turbines is currently not well quantified and the factors involved are not fully understood. In order to make more informed decisions regarding whether deployment of CM for wind turbines is economically justified, a refined set of probabilistic models capturing the processes involved are presented. Sensitivity of the model outputs with respect to variables of interest are investigated within the bounds of published data and expert opinion. The results show that the levels of benefit are dependent on a variety of factors including wind profile, typical downtime duration and wind turbine sub-component replacement cost.
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Citations
Cited by
The prediction and diagnosis of wind turbine faults
Kusiak, A., & Li, W. (2011) · Renewable Energy · doi:10.1016/j.renene.2010.05.014 · Cited by 454
Afridi, Y. S., Ahmad, K., & Hassan, L. (2022) · International Journal of Energy Research · doi:10.1002/er.7100 · Cited by 140
Metadata
- Authors
- McMillan, D., & Ault, G. W.
- Venue
- IET Renewable Power Generation
- Published
- 2008-03-10
- Volume
- 2
- Issue
- 1
- Pages
- 60-72
- ISSN
- 1752-1416 (ISSN-L)
- 1752-1424
- OpenAlex
- W2027883030
- Cited by
- 161
- References
- 0
- FWCI
- 26.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Condition monitoring benefit for onshore wind turbines: sensitivity to operational parameters. https://app.cochat.ai/papers/10.1049/iet-rpg:20070064
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- McMillan, D., & Ault, G. W. (2008). Condition monitoring benefit for onshore wind turbines: sensitivity to operational parameters. IET Renewable Power Generation. https://doi.org/10.1049/iet-rpg%3A20070064
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
