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Record W4409356532 · doi:10.1109/tpwrd.2025.3559399

Examination of EMT-Type Impedance Scanning Techniques for Small Signal Stability Assessment of Inverter Based Resources

2025· article· en· W4409356532 on OpenAlexafffund
Lei Meng, Ulas Karaagac, Ahda P. Grilo, Jean Mahseredjian, Keijo Jacobs

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsPolytechnique Montréal
FundersMitacs
KeywordsElectrical impedanceInverterElectronic engineeringStability (learning theory)SIGNAL (programming language)Materials scienceComputer scienceElectrical engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

Modern power grids that incorporate inverter-based resources (IBRs) may be vulnerable to persistent oscillations and instability incidents, which jeopardize the reliable operation of the system. Unstable operation conditions can be identified via impedance-based stability assessment (IBSA) methods, provided that accurate frequency-dependent impedance models of the system are available. A promising approach for obtaining such impedance models is to use time-domain simulation scanning techniques based on electromagnetic transient (EMT)-type software. This paper studies and compares five EMT-type impedance scanning methods in terms of their application procedures, analytical relationship between obtained impedances, IBSA accuracies, and computational burdens. Critical technical notifications and guidelines for the selection and application of these scanning methods, considering different types of instabilities, are provided. For accuracy assessment, the IBSA results are compared with EMT simulations on a practical test system.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.661
Threshold uncertainty score0.505

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 head0.015
GPT teacher head0.243
Teacher spread0.227 · 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 designBench or experimental
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

Citations3
Published2025
Admission routes2
Has abstractyes

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