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Record W2896956345 · doi:10.1109/sege.2018.8499500

Dynamic Analysis and Improved Control Design of a Grid-Connected Converter with Flexible Multi-Sequence Reactive Current Injection

2018· article· en· W2896956345 on OpenAlexaff
Shahed Mortazavian, Yasser Abdel‐Rady I. Mohamed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsControl theory (sociology)GridController (irrigation)Computer scienceFault (geology)Sequence (biology)ConvertersNonlinear systemAC powerVoltageCurrent (fluid)Stability (learning theory)EngineeringControl (management)Electrical engineeringMathematics

Abstract

fetched live from OpenAlex

The recently developed grid codes suggest the injection of a flexible positive- and negative-sequence reactive current components proportional to the magnitude of the voltage dip to help the power system ride through the unsymmetrical faults. However, to the best of the authors' knowledge, the detailed dynamic analysis of the grid-connected converter dynamics with the flexible positive- and negative-sequence current injection function considering the impact of the grid strength and the converter control parameters are not reported in the literature. To fill in this gap, first, a linear model of the augmented nonlinear system dynamics is developed and the small-signal stability analysis is performed on the system dynamic behaviour. Second, a new and effective model-based controller parameters design method is proposed to maintain the system stability during and after the fault with the consideration of the mutual interaction among different system controllers. Time-domain simulations validate the accuracy and the effectiveness of the developed method.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.411

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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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