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Record W2056099170 · doi:10.1109/psce.2006.296265

Wind Power Impact on System Frequency Deviation and an ESS based Power Filtering Algorithm Solution

2006· article· en· W2056099170 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsWind powerFrequency deviationElectric power systemRenewable energyPower (physics)Computer scienceControl theory (sociology)Environmental scienceEngineeringElectrical engineeringAutomatic frequency controlPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Wind power is the fastest growing renewable energy. However due to its stochastic nature, fluctuating wind power results in adverse impacts on power systems, including system frequency deviations. Study on system frequency response in this paper shows power systems are more sensitive to the medium frequency power fluctuations (between 0.01 and 1 Hz), while the majority of wind power fluctuations are located in that regions and below. For small standalone power systems, even a modest wind penetration will lead to considerable system frequency deviation by the wind. To diminish the wind power impact on system frequency, an energy storage system (ESS) based wind power filtering algorithm is proposed in this paper, aimed at attenuation of those medium frequency fluctuations. Electromagnetic transient simulation results quantitatively demonstrate the effectiveness of this algorithm; the wind power is smoothed out and the system frequency deviations are limited to an acceptable level

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 score0.299
Threshold uncertainty score0.796

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 head0.005
GPT teacher head0.205
Teacher spread0.201 · 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

Quick stats

Citations100
Published2006
Admission routes1
Has abstractyes

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