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Record W4317553593 · doi:10.1109/tpwrd.2023.3238422

Compensation Method for Parallel and Iterative Real-Time Simulation of Electromagnetic Transients

2023· article· en· W4317553593 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2023
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsConvertersCompensation (psychology)Computer scienceIterative methodElectronic engineeringElectric power transmissionTransmission lineComputationNonlinear systemVoltageLine (geometry)Control theory (sociology)EngineeringElectrical engineeringAlgorithmTelecommunications

Abstract

fetched live from OpenAlex

Parallelization allows accelerating the computation of Electromagnetic Transients (EMTs). It can rely on the natural propagation delay of transmission lines (line-delay) to decouple a network into sub-networks without any loss of accuracy. Other techniques have to be used when there is no line-delay available. This paper presents one of them, using the Compensation Method (CM). This method is applied to decouple and parallelize real-time EMT simulations. The main novelty towards previous works is the introduction of an iterative version of CM to handle nonlinearities. The performance and accuracy of CM is studied through test cases, including practical distribution and High Voltage Direct Current (HVDC) networks. Hardware-In-the-Loop (HIL) setups with Line-Commutated Converters (LCC) and Voltage Source Converters (VSC) are used to test the iterative CM on practical real-time nonlinear cases.

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.

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 score0.809
Threshold uncertainty score0.637

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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