Operations & Maintenance Optimization of Wind Turbines Integrating Wind and Aging Information
Bibliographic record
Abstract
Operations & maintenance (O&M) of wind turbines (both onshore and offshore) are heavily affected by weather conditions, particularly wind conditions. Current O&M models focused mainly on negative impacts of wind conditions on turbine reliability and maintenance, while ignoring potential maintenance opportunities emerging from dynamic wind velocities. This article addresses this issue by constructing a novel weather-centered O&M framework, integrating wind impacts on: (a) energy production, and (b) maintenance plans. Both the positive (maintenance opportunities) and negative impacts (maintenance delays) of wind conditions are quantified in the framework. Accordingly, a weather-centered opportunistic maintenance policy is developed to enable a flexible maintenance resource allocation. The maintenance model is formulated, and analytical properties regarding optimal maintenance ages are discussed. Furthermore, the net revenue of wind turbines is evaluated using the performance-based contracting (PBC), which captures dynamics of both power generation and operational costs. Experimental results demonstrate the superior performance of our framework in revenue improvement, particularly when facing high failure risks and production losses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".