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Black-Box Impedance Identification and Modeling for Time-Domain Transient Analysis of Power Electronics-Based Energy Conversion Systems

2024· article· en· W4394995429 on OpenAlexafffund
Taleb Vahabzadeh, Seyyedmilad Ebrahimi, Juri Jatskevich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransient (computer programming)Power electronicsElectrical impedanceIdentification (biology)Time domainElectronicsTransient analysisBlack boxEnergy (signal processing)Computer scienceElectronic engineeringFocused Impedance MeasurementElectrical engineeringTransient responseEngineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

The high penetration of power electronic converters in power systems imposes new dynamics and a wide range of challenges for system-level transient and stability assessment. The broad time scale of transient phenomena, diverse methodologies of controllers, nonlinearities, and reduced system transparency associated with power electronic converters pose new challenges to modern power systems and limit the application of physics-based modeling for transient and stability analysis. In this paper, using small-signal perturbation injection frequency scanning, the small-signal impedance/admittance-based models (I/ABMs) of the power-electronic-based subsystem are identified using the black-box impedance identification approach. The continuous time I/ABMs are then interfaced with the external system and used for the small-signal transient analysis (SSTA) and to perform time-domain simulations. The proposed approach is demonstrated on a power-electronic-based wind energy conversion system implemented in PSCAD/EMTDC and its accuracy and efficiency is shown for time-domain simulations.

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: none
Teacher disagreement score0.947
Threshold uncertainty score0.319

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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