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Record W7087529774 · doi:10.1016/j.epsr.2025.112326

SFA-EMT hybrid simulation of power systems: Application to HVDC systems

2025· article· en· W7087529774 on OpenAlexafffund

Bibliographic record

VenueElectric Power Systems Research · 2025
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of British ColumbiaBurnaby Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInterfacingTransient (computer programming)Interface (matter)Stability (learning theory)Benchmark (surveying)HarmonicsPower (physics)Transmission (telecommunications)Protocol (science)

Abstract

fetched live from OpenAlex

• Hybrid closed-form SFA/EMT solution with no iterations. • Maintains high accuracy and stability with large integration steps. • Arbitrary ratio of integration steps between the AC and EMT subsystems. • Automatically accounts for the direct injection of harmonics into the AC network. This paper presents the application of a novel hybrid multirate protocol to interface a Shifted Frequency Analysis (SFA) solution with an Electromagnetic Transients (EMT) solution. Using the Multi Area Thévenin Equivalent (MATE) framework, the protocol enables the direct interfacing of SFA and EMT solutions without time step delays, iterations, or the use of transmission lines to decouple the solutions. The protocol adds a parallel EMT solution to track both the real and imaginary parts of the EMT solution. This allows for a direct interface to the complex-number SFA solution. The proposed hybrid approach allows for large time steps in the SFA solution and does not require the time steps of the SFA and EMT systems to be multiples of each other. The protocol has been previously validated in a transient stability study, and it is applied in this paper to power electronics devices in the EMT subsystem using a modified CIGRE HVDC benchmark system. The use of SFA and the multirate nature of the solution offers significant computational savings for large power systems compared to an EMT-only solution.

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)0.003
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 score0.863
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
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 head0.017
GPT teacher head0.323
Teacher spread0.306 · 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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