Dynamic Phasor-Based Modeling and Analysis of Dual-Loop Controlled DC-DC Converters
Bibliographic record
Abstract
To design efficient and reliable direct current-based energy systems, telecommunication equipment, transportation systems, and healthcare devices, accurate DC-DC converter models are required. In addition, computationally efficient models are required to expeditiously simulate large DC systems (with multiple converters). In this paper, the dynamic phasor (DP) method is used to develop computationally efficient multi-frequency average models of dual-loop controlled DC-DC converters (buck, boost, and buck-boost) which are suitable for accurate modeling and analysis of ripples. By recognizing that high frequency components of inductor current and capacitor voltage are small compared to DC components, in addition to being equal to zero over a switching cycle, the control system is designed to target the average value of current and voltage. This simplifies the control process compared to existing DP-based closed-loop models of DC-DC converters. Small-signal modeling technique is used to obtain suitable control gains. The DP-based DC-DC converter models built on MATLAB/Simulink are validated against detailed models built on Simulink/Simscape. Simulation results confirm the effectiveness of the proposed control strategy as well as the huge computational advantage of DP-based DC-DC converter models over detailed models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".