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Record W2875838175 · doi:10.1109/tie.2018.2850002

Grid-Supporting Inverters With Improved Dynamics

2018· article· en· W2875838175 on OpenAlexaff
S. Ali Khajehoddin, Masoud Karimi-Ghartemani, Mohammad Ebrahimi

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInertiaInverterGovernorComputer scienceSynchronous motorTransient (computer programming)GridControl engineeringControl theory (sociology)Energy storageElement (criminal law)EngineeringControl (management)VoltageElectrical engineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

A new inverter control approach, called enhanced virtual synchronous machine (eVSM), is proposed based on the VSM concept. Unlike existing VSM approaches, the eVSM does not emulate the rotating inertia based on an assumption of the unlimited energy storage, but it deploys the physically existing inertia of the dc-link element. The eVSM adopts an innovative way of enlarging the inertia utilization range, which obviates the need for having a large dc-link element or a dedicated battery storage system, while still providing the same inertia response of an equivalent synchronous machine. Theoretical developments and numerical results presented in this paper confirm that the proposed eVSM can present a stabilizing support to the grid similar to a synchronous machine despite the small size of its dc-link element. Moreover, its transient responses can be improved beyond those of conventional synchronous machines thanks to the possibility of more flexibly adjusting damping and governor functions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.194
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), 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

Citations104
Published2018
Admission routes1
Has abstractyes

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