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Record W2914501353 · doi:10.11575/prism/35718

Modeling and Evaluation of Wind Turbine Operational Strategies During Icing Events

2019· dissertation· en· W2914501353 on OpenAlex
Shannon Hildebrandt

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOpen MIND · 2019
Typedissertation
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsnot available
FundersNatural Resources CanadaAlberta InnovatesPennsylvania State University
KeywordsIcingTurbineEnvironmental scienceEngineeringAeronauticsAerospace engineeringMarine engineeringMeteorologyGeography

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.317
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it