A State-Space Modeling Approach for the FPGA-Based Real-Time Simulation of High Switching Frequency Power Converters
Bibliographic record
Abstract
A comprehensive approach to the real-time simulation of power converters using a state-space representation is covered in this paper. Systematic formulations of state-space equations as well as a new switch model are presented. The proposed switch model exhibits a natural switching behavior, which is a valuable characteristic for the real-time simulation of power converters, thereby allowing individual treatment of switching devices irrespective of the converter topology. Successful implementations of the proposed switch model on a field programmable gate array (FPGA) device are reported, with two alternative approaches: 1) precomputing network equations for all switch state combinations and 2) solving network equations on-chip using the Gauss–Seidel iterative method. A two-level three-phase voltage source converter is implemented using the first approach, with a time step of 80 ns and a switching frequency of 200 kHz. Ideal and nonideal boost converters are also implemented on FPGA using the second approach, with a time step of 75 ns and a switching frequency of 20 kHz. Comparison with SPICE models shows that the proposed switch model offers very satisfactory accuracy and precision.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".