Predicting Switch ON/OFF Statuses in Real Time Electromagnetic Transients Simulations with Voltage Source Converters
Bibliographic record
Abstract
Power converters in system-level real-time simulations are usually emulated by using an L/C associated discrete circuit (L/C-ADC)in a fixed time-step simulation. However, there are several potential inaccuracies in L/C-ADC based simulation results: unrealistically high virtual loss especially at high PWM frequencies, fictitious current oscillations between the Land C represented devices, and limitations in the impedance ratio between OFF and ON switch representations. Therefore, there are potential benefits from using switched-resistance representations of electronic switch devices. Fortunately, such simulation has recently become feasible due to the higher performance of newer computer processor cores. However, the switched-resistance representation of switches requires reliable prediction of the ON/OFF statuses of the switch devices before each simulation time-step. This paper describes a method for highly reliable prediction of ON/OFF switch statuses for voltage source converters with switched-resistance switch representations in fixed time-step simulation. Real-time simulation results are presented for systems containing 2-level and 3-level voltage source converters. The technique could be practised in non-real-time simulation for much more complicated converters.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".