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Record W1688376552

Hardware-in-the-Loop simulation of a complex AC-fed motor drive with triple active front-end 3-level rectifiers and induction motor drive using an intra-step-parallel state-space-nodal solver

2011· article· en· W1688376552 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

VenueEuropean Conference on Power Electronics and Applications · 2011
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsInduction motorComputer scienceSolverTransformerControl theory (sociology)VoltageElectrical engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the Hardware-in-the-Loop (HIL) simulation results of a very complex AC-fed induction motor drive. The drive is composed of an AC-stage composed of 3 saturable zig-zag transformers in series, each connected to a 3-level NPC inverter and DC-link with RLC filter. The DC-link feeds another 3-level NPC-based induction motor drive and also comprises a precharge circuit. All switching devices of the circuit are controllable from the I/O points of the real-time simulator. Due to the high complexity of this drive and the requirement for full fault simulation capability, a new electric circuit solver called State-Space Nodal, based on state-space and nodal approach was used from within SimPowerSystems. The new algorithm is the first known example of an electric system solver algorithm that parallelizes the network equation on multi-core micro-processors without delays using computing threads. The use of parallel computing threads enables to gain up to 33% on computational time when compared to standard sequential execution of the same algorithm.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.762

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 head0.049
GPT teacher head0.253
Teacher spread0.204 · 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