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Dynamic Droop Control for Wind Turbines Participating in Primary Frequency Regulation in Microgrids

2017· article· en· 141 citations· W2609828511 on OpenAlex· 10.1109/tsg.2017.2696339

Why is this work in the frame?

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

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.

Full frame distilled prediction

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.

Candidate categories
none
Consensus categories
none
Domain
Candidate signal: noneConsensus signal: none
Study design
Candidate signal: Simulation or modelingConsensus signal: Simulation or modeling
Genre
Candidate signal: EmpiricalConsensus signal: none
Teacher disagreement score
0.545
Threshold uncertainty score
0.843
Validation status
machine_predicted_unvalidated · codex-gemma-dda1882f352a

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)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

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.

Opus teacher head0.009
GPT teacher head0.226
Teacher spread
0.217 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

Wind power will provide a significant portion of electricity generation in the near future. This significant role requires wind power generators to contribute to the system frequency regulation. The droop method is one of the most popular methods to be implemented in these generators to mimic the governors of conventional generators and contribute to both transient and steady-state frequency regulation. However, the unpredictability of the variable wind speed complicates this implementation. In the present wind-based droop methods, the maximum allowable droop gain is a function of the wind speed. This dependency means either that the entire available capacity of the wind generator will not be used or that instability will threaten the implemented droop wind generators. This paper proposes the efficiency droop, a new droop-based method, which can be tuned regardless of the wind speed. Small-signal analyses are used to study the method in depth and compare its influences on both the transient and steady-state frequency performance to the influence of the present methods while adopting minimum approximation. Detailed time domain analyses are used to verify the analytical results.

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.

The record

Venue
IEEE Transactions on Smart Grid
Topic
Microgrid Control and Optimization
Field
Engineering
Canadian institutions
University of Alberta
Funders
not available
Keywords
Voltage droopControl theory (sociology)Wind powerTransient (computer programming)Automatic frequency controlWind speedEngineeringPermanent magnet synchronous generatorPower (physics)Generator (circuit theory)Computer scienceControl (management)VoltageVoltage regulatorElectrical engineeringMeteorologyPhysics
Has abstract in OpenAlex
yes