Modeling and Evaluation of Wind Turbine Operational Strategies During Icing Events
Bibliographic record
Abstract
Cold climates around the world are seeing increasing investment in wind power generation. The benefits of cold regions, however, come with unique challenges that are not experienced by wind turbines in more temperate regions. The accumulation of ice on wind turbine blades in particular can reduce power production due to aerodynamic inefficiencies and turbine shutdowns. To gain a better understanding of the extent to which these challenges are faced across Canada, the author ran a Survey in 2017 of 43 wind farms across the country. Results were presented at the 2018 CanWEA O&M Summit, and discussions that followed highlighted an important and unanswered question: When an icing event is detected or predicted at a wind farm, is it better to pause the turbines during the event or maintain power production? How much less ice is accumulated if the wind turbines are paused, and how does this impact power production? To answer these questions, the Ice and Power Model described herein was developed. Wind turbine characteristics and icing event conditions are taken as inputs, and blade ice accumulation, aerodynamic impacts, and power production impacts are produced as outputs. The model consists of three components: (1) ice accumulation, (2) aerodynamic analysis, and (3) power curve estimation. Upon validation, the model was used to estimate and analyze the blade ice accumulation on the NREL 1.5 MW reference wind turbine for five icing events, in which the input parameters of far-field wind speed, air temperature, cloud liquid water content, and droplet mean volume diameter were varied. For each icing event, two simulations were executed with the model where: (a) the wind turbine maintains operation during the icing event and (b) the wind turbine is paused for the duration of the icing event. The resulting ice accumulation, impacts to blade aerodynamics, and impacts to power production capabilities following the icing event were compared. The results provide evidence that while pausing turbines does indeed result in significantly less ice accumulation, the impact to power production capabilities following the icing event is not significant enough to justify cutting power production to zero for short events.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".