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Record W2614005013 · doi:10.24084/repqj09.510

An Optimal Virtual Inertia Controller to Support Frequency Regulation in Autonomous Diesel Power Systems with High Penetration of Renewables

2017· article· en· W2614005013 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

VenueRenewable Energy and Power Quality Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsInertiaPenetration (warfare)Renewable energyElectric power systemDiesel fuelFrequency regulationController (irrigation)Automotive engineeringControl theory (sociology)Computer sciencePower (physics)EngineeringControl (management)PhysicsElectrical engineeringBiologyOperations research

Abstract

fetched live from OpenAlex

This paper addresses the problem of frequency control in autonomous diesel-based power system with high penetration of renewables. Usually, small power systems with high penetration of renewable energies are supplied by one or two small diesel generators, resulting in a system with a relatively low moment of inertia, and which can be susceptible to significant frequency variations. However, frequency regulation can be supported by modifying the inertial response of the system in an artificial way, i.e., by adding a virtual inertia. The latter can be performed by controlling the power electronics interface of a distributed generator or an energy storage unit. In this work, a controller is designed to provide the optimal virtual inertia which minimizes, according to the proposed performance index, variations in the fundamental frequency as well as in the power flow through the energy storage system. The optimal controller is compared by simulations with other virtual inertia control strategies.

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)0.001
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: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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