Comparative Study of Adaptive Hamiltonian Control Laws for DC Microgrid Stabilization: An Fuel Cell Boost Converter
Bibliographic record
Abstract
Future smart grids can be seen as a system of interlinked microgrids, including small-scale local power systems. They consist of main power sources, external loads, and energy storage devices. In these microgrids, the negative incremental impedance behavior of constant power loads (CPLs) is of major concern since it can lead to instability and oscillations. To cope with this issue, this article aims to propose a comparative study of adaptive Hamiltonian control laws, also known as interconnection and damping–assignment–passivity–based controllers (IDA-PBC). These control laws are developed to ensure the stability of the DC output voltage of a boost converter supplied by a proton exchange membrane fuel cell (PEMFC) source. To validate the develop control laws, experiments have been performed on a fit test bench including a real 2.5 kW PEMFC stack (hydrogen is supplied by a reformer engine), a DC-DC step-up circuit, and a real-time controller dSPACE (implementation of the control laws). Moreover, a comparative study has been carried out between the proposed three adaptive Hamiltonian control laws and a classic linear cascaded proportional–integral (PI) control law. The obtained results by simulations through MATLAB/SimulinkTM and experimentally have allowed demonstrating that the third Hamiltonian control law presents the best performances over the other control laws.
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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) | 0.000 | 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 | 0.000 | 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".