Impact of wind turbine nominal power limitation over wind turbine blade remaining useful life and its economic consequences
Bibliographic record
Abstract
The present paper investigates the effects of wind turbine nominal power limitation on the remaining useful life of turbine blades. It also looks at the economic impact of this limitation. In this context, the paper provides wind turbine owners and operators with an overview of how to potentially extend the remaining useful life of wind turbine blades and lays out the economic benefits that can be achieved via the modulation of nominal power. In investigating wind turbine blade damage, prior studies focused mainly on predictive models based on the 10 minutes SCADA data wind speed history, without however, trying to protract the remaining useful life of the blades. Only a handful of papers have explored the possibility of increasing the remaining useful life by adjusting the start-up and shutdown procedures with poor results. It would appear that wind turbine blade fatigue damage mainly increases when the wind turbine is in a power production regime, and the mechanical stresses associated with this regime are a function of the nominal power of the wind turbine. The present work therefore investigates the impacts of nominal power changes on both the remaining useful life of wind turbine blades and the economic value of the wind turbine in a bid to identify an optimal control mode. The wind turbine blade damage evaluation is based on 10 minutes SCADA data and the FAST simulation tool with the ultimate goal of providing wind turbine operators with an easy application. The damage evaluation is then applied considering different nominal power levels for the same wind turbine model in order to see the resulting impact on the remaining useful life. This project therefore takes a pioneering approach by proposing a remaining useful life optimization tool to wind turbine operators, in effect, a decision-making tool regarding which exploitation strategy to adopt.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".