Board 68: Work in Progress: LabSim: An Ancillary Simulation Environment for Teaching Power Electronics Fundamentals
Bibliographic record
Abstract
Abstract Switch-mode power conversion is one of the most crucial topics in a modern undergraduate electrical energy systems curriculum. The importance and ubiquity of switch-mode power converters, however, are matched by their complexity. Students are expected to have developed a rigorous understanding of electrical circuits, semiconductor physics, signal processing, control theory, digital logic, and wave mathematics before being introduced to power electronics. Students at our institution are introduced to fundamental concepts in lectures then they put them into practice in hands-on labs, which are limited to three-hour-long experiments conducted in a strictly controlled environment due to safety concerns. This leaves little room for exploration and independent trial-and-error. We have developed LabSim, an out-of-the-box functional software implementation of the switch-mode converters studied in class, in order to provide students with the opportunity to practically explore power electronics fundamentals and experiment at their own pace. LabSim is implemented in Simulink using visual PLECS blocks, an approach that ensures students do not have to spend significant time learning new software or navigating complex mathematical models. A pilot run of LabSim was conducted over the course of a semester, with students being provided the models in pace with the relevant lecture and lab material. We present a detailed description of the LabSim implementation and the specific shortcomings it aims to address within our introductory power electronics course. We also present and analyze the positive results of the LabSim pilot project as indicated by a student survey emphasising learning impact and workload management.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.020 |
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".