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Record W4393253182 · doi:10.1109/tpwrd.2024.3382843

Power Swing in Systems With Inverter-Based Resources—Part II: Impact on Protection Systems

2024· article· en· W4393253182 on OpenAlexaff
Mohamad‐Amin Nasr, Ali Hooshyar

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2024
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSwingElectric power systemInverterPower-system protectionElectrical engineeringComputer scienceReliability engineeringPower (physics)EngineeringElectronic engineeringPhysicsVoltageMechanical engineering

Abstract

fetched live from OpenAlex

After introducing and analytically proving the distinctive dynamics associated with power swing of inverter-based resources (IBRs) in Part I, Part II of this paper investigates the implications of such dynamics for power system protection. The paper sheds light on some of the major consequences of IBRs' unique power swing patterns. The study employs theoretical analysis and detailed PSCAD/EMTDC simulation models to unveil the shortcomings of the conventional wisdom regarding the power swing characteristics of IBRs. The paper offers new insights into the operation of transmission line relays during power swing conditions. This part of the study is supported by extensive testing of commercial relays. The findings of this paper will provide guidance for future IBR interconnection standards, the design of inverter control schemes, the power swing detection methods used by relays, and the appropriate relay settings.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2024
Admission routes1
Has abstractyes

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