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Record W2621129841 · doi:10.1049/iet-gtd.2016.1574

Contribution to stability analysis of power hardware‐in‐the‐loop simulators

2017· article· en· W2621129841 on OpenAlexaffabout
Olivier Tremblay, Handy Fortin‐Blanchette, Richard Gagnon, Y. Brissette

Bibliographic record

VenueIET Generation Transmission & Distribution · 2017
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsÉcole de Technologie SupérieureHydro-Québec
Fundersnot available
KeywordsStability (learning theory)Loop (graph theory)Computer sciencePower (physics)Hardware-in-the-loop simulationPower analysisControl engineeringControl theory (sociology)EngineeringEmbedded systemControl (management)MathematicsArtificial intelligenceAlgorithmPhysicsMachine learning

Abstract

fetched live from OpenAlex

This study establishes a new basis for understanding the stability of power hardware‐in‐the‐loop (PHIL) systems considering their hybrid (analogue/digital) nature. Such systems are known to have closed‐loop stability issues due to delays between the simulator and the power amplifier (PA). This work demonstrates that the conventional method for determining the stability criterion, which considers the system as a continuous model, is not appropriate. A new method of assessing the stability of a PHIL system based on discrete‐time impedance frequency responses is thus presented. Hydro‐Québec's Research Institute will use this innovative approach for the development of its own PHIL system, which will involve connecting the institute's real‐life experimental distribution test line to its large‐scale real‐time digital simulator through a 25‐kV, 10‐MVA PA. The validity of the new method is demonstrated for simulation models of a well‐known inductive system and of a distribution feeder connected to a large‐scale power system.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0010.000
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 head0.019
GPT teacher head0.263
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

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
GenreMethods

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

Citations29
Published2017
Admission routes2
Has abstractyes

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