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Record W4414224024 · doi:10.1016/j.epsr.2025.112214

Dynamic equivalencing of power systems using bus impedance matrix

2025· article· en· W4414224024 on OpenAlexafffund
Mahesh Rathnayake, Gayan Wijeweera, B.A. Archer, U.D. Annakkage

Bibliographic record

VenueElectric Power Systems Research · 2025
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsManitoba HydroUniversity of Manitoba
FundersMitacsManitoba Hydro
KeywordsElectric power systemEquivalent impedance transformsReliability (semiconductor)Impedance parametersStability (learning theory)Control theory (sociology)Set (abstract data type)Boundary (topology)Power (physics)Electrical impedance

Abstract

fetched live from OpenAlex

In real-time power system operations, frequent assessment of system operating limits for large, interconnected networks through dynamic simulations is critical for maintaining stability and reliability for system operators. To address the associated computational challenges and reduce simulation time, portions of the network must be represented using equivalent models. This paper introduces a methodology that involves solving a set of linear equations to develop equivalent models at regular intervals, reflecting real-time system conditions. The proposed structured and repeatable methodology facilitates the frequent updating of the external equivalent model while limiting the computational burden to practically acceptable levels. The dynamic simulation results for the study area, comparing the full system model and the reduced model derived through the proposed methodology, demonstrate a high degree of consistency. While the proposed methodology exhibits certain limitations, it also presents notable strengths, which can be further explored and refined to enhance the accuracy of the reduced model.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.587
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.354
Teacher spread0.328 · 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.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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