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Record W4405440190 · doi:10.1109/tsg.2024.3518358

Suppression of the Dynamic Interactions Between a VSG and Dynamic Loads Under Weak-Grid Conditions

2024· article· en· W4405440190 on OpenAlexaff
Mahmoud A. Elshenawy, Amr Radwan, Yasser Abdel‐Rady I. Mohamed, Ehab F. El‐Saadany

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2024
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of Alberta
FundersCharotar University of Science and Technology
KeywordsGridDynamic demandControl theory (sociology)Dynamic load testingComputer sciencePhysicsEngineeringPower (physics)MathematicsStructural engineeringControl (management)

Abstract

fetched live from OpenAlex

The virtual synchronous generator (VSG) features more non-oscillatory stable performance under weak grid conditions than stiff grid conditions. However, as shown in this paper, a local industrial load with induction motors jeopardizes this fact and superimposes low-frequency oscillations on the outputs of the weak grid-connected VSG. Detailed small-signal models of a weak grid-connected VSG are developed and compared in the absence and presence of a local industrial load. This study shows that dynamic loads, e.g., induction motors, weaken the VSG damping and limit the stability ranges of the droop gains and virtual inertia constant, limiting the VSG’s features and grid-supporting capabilities. Therefore, an active compensator is proposed to regain the VSG’s damping and stability under weak grid conditions. With the proposed damping and stabilization compensator, the VSG dynamics and stability are highly improved under wide ranges of droop gains and virtual inertia. Multiple offline simulations and real-time tests are carried out to justify the existence of dynamic interactions between a VSG and an industrial load, considering diverse load compositions, and verify the proposed compensator’s effectiveness in enhancing the overall system performance and stability under practical conditions, such as load switching, faults, and grid angle disturbances.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.604

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)0.0000.000

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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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