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Record W1996697129 · doi:10.1109/epepemc.2012.6397412

Modeling, analysis and control design complexities in future electric energy systems

2012· article· en· W1996697129 on OpenAlexfundno aff
Marija Ilić, Miloš Cvetković, Kevin D. Bachovchin, Qixing Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsnot available
FundersRoyal Canadian Geographical Society
KeywordsElectric power systemWind powerComputer scienceStability (learning theory)Automatic frequency controlControl systemControl engineeringTelecommunicationsEngineeringPower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

In this paper we suggest that a successful evolution of future electric energy systems will be critically dependent on having a systematic framework for communications and control architectures. These must be capable of ensuring stability and quality of frequency and voltage response in qualitatively different ways than it is currently being done. The main drivers of such change are described first. In particular, the focus of this paper is on the stability problems caused by the wind turbulence in systems with significant wind penetration. Next, solutions to the small signal stability problem, frequency regulation and large wind surge problem are proposed. The solutions are based on the concepts which honor the change in system dynamics caused by the new technologies. The claims are illustrated using realistic data for a small island power system in Flores, Portugal.

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)0.001
metaresearch head score (Gemma)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
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 head0.007
GPT teacher head0.167
Teacher spread0.160 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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