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Record W1624814911 · doi:10.1109/pesgm.2015.7285635

Stability analysis of converter-connected battery energy storage systems in the grid

2015· article· en· W1624814911 on OpenAlexaff
Damon Bazargan, Shaahin Filizadeh, Ani Gole

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsInternal resistanceBattery (electricity)Energy storageVoltagePower (physics)Electrical engineeringGridState of chargeStability (learning theory)Computer scienceControl theory (sociology)EngineeringAutomotive engineeringPhysicsMathematics

Abstract

fetched live from OpenAlex

Summary from only given. This paper analyzes the stability of a battery energy storage system (BESS) connected to the grid using a power electronic interface. It is shown that the internal resistance and internal voltage of the battery affect system stability. Variations in these parameters may occur due to aging and changes in the state-of-charge (SoC). Using average-value modeling, this problem is framed into a nonlinear system formulation and the region of stability as a function of the internal resistance and the internal voltage of the battery is determined. This paper also extends its results in determining the configuration of a battery pack in terms of the number of battery cells in series and parallel to prevent instability while meeting demand power requirements.

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: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.191

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 head0.014
GPT teacher head0.182
Teacher spread0.169 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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