Modified Lyapunov-Based Model Predictive Direct Power Control of an AC-DC Converter with Power Ripple Reduction
Bibliographic record
Abstract
This paper presents a modified Model Predictive- Direct Power Control scheme (MP-DPC) for an AC-DC power converter with the help of Lyapunov constraints. Conventional MP-DPC has the disadvantage of using a PI controller, which makes the responses sluggish and has large power ripples. The Lyapunov function has an added advantage of further improving the stability with the help of inequality constraints over a bounded region by finding a local optimal point rather than a global one. The disturbance vector used in the Lyapunov function is responsible for selecting the best possible future switching state, which is the reference voltage vector for space vector (SV-PWM). The proposed scheme achieves a better dynamic response at the output DC voltage along with reduced ripples in both real and reactive power. Simulation results are presented to verify that the proposed Lyapunov based MP-DPC scheme (which has no PI) has an improved dynamic response and lower power ripple compared to the conventional MP-DPC (with PI).
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".