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Record W2554861786 · doi:10.1109/pesgm.2016.7741703

New impedance reshaping algorithm for grid-connected DGs

2016· article· en· W2554861786 on OpenAlexaff
Maher A. Azzouz, Ehab F. El‐Saadany

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectrical impedanceRobustness (evolution)GridDamping factorComputer scienceControl theory (sociology)Bandwidth (computing)Output impedanceImpedance matchingElectronic engineeringEngineeringControl (management)MathematicsElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper proposes a new control algorithm for optimally reshaping distributed generation (DG) output impedance so that the system damping and bandwidth are maximized. Such impedance reshaping is essential for managing variations in grid impedance and changes in DG operating conditions. The proposed control algorithm involves two design stages. First, the multivariable DG output impedance is derived mathematically. The grid impedance is also estimated to formulate the impedance stability criterion. Second, a multi-objective programming is formulated to maximize the system damping and bandwidth. Real-time simulations are performed to investigate the effectiveness and robustness of the proposed control algorithm.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.949
Threshold uncertainty score0.236

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 head0.006
GPT teacher head0.188
Teacher spread0.183 · 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 designOther design
Domainnot available
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

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