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Record W4394707692 · doi:10.1109/icjece.2024.3351152

Optimal Tuning of Virtual Inertia Control for Frequency Regulation of Microgrid

2024· article· en· W4394707692 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMicrogridInertiaFrequency regulationAutomatic frequency controlControl theory (sociology)Control (management)Computer scienceControl engineeringEngineeringElectric power systemPower (physics)PhysicsArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

The integration of renewable energy sources in modern microgrid power systems has a significant impact on frequency stability due to reducing inertia and damping coefficient. This article employs a virtual inertia control (VIC) based on frequency deviation derivatives to emulate the system inertia and damping coefficient characteristics of traditional synchronous generators. Coordination between the global controller (load frequency control) and the VIC is implemented. The parameters of both the secondary and virtual control are tuned using a novel hybrid sparrow search algorithm with mountain gazelle optimizer algorithm. The simulation results demonstrate a substantial improvement in mitigating the low inertia of the power system when exposed to consecutive rapid load changes, utilizing the suggested algorithm on comparing with the hybrid sparrow search algorithm based on grey wolf optimizer.

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 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 score0.871
Threshold uncertainty score0.359

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 head0.003
GPT teacher head0.152
Teacher spread0.149 · 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