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Record W2156637035 · doi:10.1109/pes.2007.385592

Real-Time Digital Hardware Simulation of Power Electronics and Drives

2007· article· en· W2156637035 on OpenAlexaff
G.G. Parma, Venkata Dinavahi

Bibliographic record

VenueIEEE Power Engineering Society General Meeting · 2007
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsField-programmable gate arrayHardware description languageVHDLComputer scienceGate arrayTransient (computer programming)Insulated-gate bipolar transistorPulse-width modulationReal-time simulationComputer hardwareElectronic engineeringEmbedded systemVoltageEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Summary form only given. This paper presents a digital hardware realization of a real-time simulator for a complete induction machine drive using a field programmable gate array (FPGA) as the computational engine. The simulator was developed using very high speed integrated circuit hardware description language (VHDL), making it flexible and portable. A novel device-characteristic based model suitable for FPGA implementation has been proposed for the 2-level 6- pulse IGBT-based voltage source converter (VSC). The VSC model is computed at a fixed time-step of 12.5 nanoseconds allowing a highly detailed and precise accounting of gating signals. The simulator also models a squirrel cage induction machine, a direct field-oriented control system, a space-vector pulse-width modulation scheme (SVPWM) and a measurement system. A multi-rate simulation of the system shows the slow (machine) as well as the fast (VSC and control) dynamic components. Real time simulation results under steady-state and transient conditions demonstrate modeling accuracy and efficiency.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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
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

Citations14
Published2007
Admission routes1
Has abstractyes

Explore more

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