Improved Stability Based on Lyapunov-Hamiltonian Control Law for Multi-Segment Converters in DC Microgrids Interconnections
Bibliographic record
Abstract
This paper presents a robust control strategy for enhancing the large-signal stability of DC microgrids with interconnected multi-segment converters. An improved Lyapunov-Hamiltonian Control Law (LHCL) is proposed to regulate the dynamic behavior of interleaved Boost/Buck converters supplied by fuel cells. By exploiting port-Hamiltonian modeling and integrating Lyapunov-based damping, the controller dynamically adapts to varying load conditions, particularly under challenging constant power load (CPL) scenarios. The proposed method ensures global stability by shaping energy flow and minimizing oscillations through analytically derived damping terms. Experimental validation on a test bench equipped with a real-time dSPACE controller demonstrates the superior performance of the LHCL approach under both constant resistance and CPL disturbances. The results confirm the method’s effectiveness in maintaining voltage stability, achieving current balancing, and ensuring robust energy management in DC microgrid interconnections.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".