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Record W3119772170 · doi:10.1109/tste.2021.3049762

Adjustable Wind Farm Frequency Support Through Multi-Terminal HVDC Grids

2021· article· en· W3119772170 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

VenueIEEE Transactions on Sustainable Energy · 2021
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsToronto Metropolitan University
FundersPower Systems Engineering Research Center
KeywordsWind powerTurbineOffshore wind powerAutomatic frequency controlComputer scienceGridFrequency gridElectrical engineeringMarine engineeringEngineeringTelecommunicationsVoltageAerospace engineeringMathematics

Abstract

fetched live from OpenAlex

In the future power systems, a large number of offshore wind farms will be connected to the AC grids through high voltage DC (HVDC) and multi-terminal DC (MTDC) grids. As wind power penetration level increases, complex grid codes and regulations will be imposed on wind turbines for frequency support. To follow any grid code and requirement for frequency support, two important features should be included in the wind turbine frequency support: i) It should be able to adjust the maximum additional power that the wind turbine temporarily provides for frequency support; ii) It should be capable of adjusting the time interval in which the wind turbine provides additional temporary power. The first feature is mainly important for reducing rate of change of frequency (RoCoF) and improving the frequency nadir while the second one is mainly important for fast frequency recovery from its nadir and improving the second frequency drop. This paper indicates that the conventional method cannot offer both of the two aforementioned features. To address this issue, two approaches are proposed for frequency support by wind turbines. The first one uses P-ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> and P-f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WF</sub> droops in each wind turbine controller, where P, ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> , and f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WF</sub> represent the wind turbine power, rotor speed, and wind farm frequency. The second method employs P-· ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> and P-f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WF</sub> droops in each wind turbine controller. Performance and effectiveness of the proposed methods are evaluated by time-domain simulation studies on an MTDC grid in the PSCAD/EMTDC software environment.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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