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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".