Robust Hamiltonian Energy Control Based on Lyapunov Function for Four-Phase Parallel Fuel Cell Boost Converter for DC Microgrid Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rapid developments in hydrogen fuel cell (FC) energy and DC microgrid systems have extended the applications of multiphase parallel interleaved step-up converters for stabilizing DC bus voltages. DC microgrid applications include vehicle systems, shipboard power systems, and more electric aircraft, which generate power at low voltage levels. The cascade architecture of a power converter in a DC microgrid may cause large oscillations and imbalance given that converters considered as loads have constant power load characteristics. In this work, output DC bus voltage stabilization and current sharing of a multiphase parallel-interleaved-FC boost converter is presented. The proposed robust controller with added integrator action is based on the Hamiltonian-Lyapunov function. The efficacy and robustness of the designed controller were successfully authenticated by experimental results obtained using a 2.5 kW prototype FC converter (via four-phase parallel-interleaved boost converters) and the dSPACE MicroLabBox platform. The main source of the FC is based on a fuel reformer engine that converts fuel methanol and water into H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> gas in a polymer-electrolyte-membrane-FC stack (50 V, 2.5 kW).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it