Coupled Atmospheric–Ice Load Model for Evaluation of Wind Plant Power Loss
Bibliographic record
Abstract
Abstract Icing is a weather phenomenon that is typical of cold climates. It impacts human activities through ice accretion on tower structures, transmission lines, and the blades of wind turbines. Icing on turbine blades, in particular, results in wind turbine performance degradation and/or safety shutdowns. The objective of this study is to explore the feasibility of using a coupled atmospheric and ice load model to simulate icing start-up, duration, and amount while also quantitatively evaluating power loss in wind plants related to icing events and mechanisms. Eight of 27 icing episodes identified for a wind plant in the Gaspé region of Québec (Canada) during the period 2008–10 were simulated using a mesoscale model (the Global Environmental Multiscale Limited-Area Model, or GEM-LAM). The simulations were verified using near-surface temperature, relative humidity, and wind speed, all of which compared well to in situ observations. Simulated wind speed, precipitation, cloud liquid water content, and median volume diameter of the droplets were used to drive ice load models to simulate the total ice load on a cylindrical structure. The three ice load models accounted for freezing rain, wet snow, and in-cloud icing, respectively, and in all three cases a sink term was added to account for melting due to radiation. The start-up and duration of ice were well captured by the coupled model, and a positive correlation was found between icing episodes and wind power reduction. This study demonstrates the improvements of the icing forecasts by using three ice load models, and provides a framework for both qualitative and quantitative evaluation of icing impact on wind turbine operations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".