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Triangular-Shaped and Simultaneous Adjustment of Inertia and Damping in VSG-based Distributed Energy Resources for Improved Frequency Response

2021· article· en· W3216478394 on OpenAlexaff
Erfan Mostajeran, Arash Safavizadeh, Seyyedmilad Ebrahimi, Juri Jatskevich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInertiaControl theory (sociology)InterfacingAutomatic frequency controlController (irrigation)Frequency responsePower (physics)Computer scienceElectric power systemEnergy (signal processing)Distributed generationEngineeringControl (management)MathematicsPhysics

Abstract

fetched live from OpenAlex

The concept of virtual synchronous generators (VSGs) has been introduced as a viable control methodology for interfacing power electronic-based distributed energy resources (DERs) with the grid. This paper investigates a family of VSG control mechanisms with variable parameters; and proposes a new approach that simultaneously adjusts both the virtual inertia and damping in order to improve the overall frequency response of the DERs. The proposed method employs simplified triangular-shaped trajectories for changing the inertia and damping coefficients. Specifically, the proposed controller simultaneously increases both the virtual inertia and damping parameters based on the measured load disturbance in the power system, and decreases them linearly with an adjustable slope, which offers a simple implementation. The effectiveness of the proposed method is verified using computer simulations of a small-scale power system including a DER and a load. It is shown that the proposed control method improves the dynamic frequency response compared to the existing state-of-the-art VSG control techniques.

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.

How this classification was reachedexpand

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.407

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 head0.004
GPT teacher head0.187
Teacher spread0.183 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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