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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".