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Hybrid Simulation and Off-the-Shelf Hardware for Efficient Real-Time Simulation Studies

2020· article· en· W3094646889 on OpenAlexaffabout
Philippe Le‐Huy, Jinan Huang, F. Guay, Innocent Kamwa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsTransient (computer programming)ReplicaReal-time simulationHardware-in-the-loop simulationComputer scienceElectric power systemTransmission systemCurrent loopTransmission (telecommunications)Control systemPower (physics)SimulationEmbedded systemComputer hardwareVoltageEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

A real-time simulation study, where a control system replica is connected to a real-time electromagnetic transient simulator in closed-loop (known as hardware-in-the-loop), is an essential tool in commissioning or refurbishment projects of major power system devices such as static var compensators, high-voltage direct-current transmission systems and other flexible alternative-current transmission systems: it allows to reduce commissioning risks and it is useful throughout the life of the system. It is however an expensive endeavor in time, manpower and equipment. The present paper aims at presenting a more affordable and efficient way to pursue realtime hardware-in-the-loop commissioning studies by using 1) hybrid simulation (electromagnetic transient and transient stability modeling) to increase the scale of the power system that can be simulated in real-time without increasing the required real-time hardware resources and 2) low-cost commercially off-the-shelf computers. To illustrate this approach, a hardware-inthe-loop experimental setup is presented and detailed: based around the control system replica of Hydro-Québec's Figuery static var compensator, this setup shows how hybrid simulation and low-cost personal computers can be used to reduce the cost of real-time simulation studies.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.493

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 head0.029
GPT teacher head0.275
Teacher spread0.246 · 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 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

Citations2
Published2020
Admission routes2
Has abstractyes

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