Non‐linear large‐signal stabiliser design for DC micro‐grids
Bibliographic record
Abstract
This study proposes a non‐linear stabiliser to suppress the current/voltage fluctuations of DC micro‐grids (DC‐MGs) caused by large transients. These transients typically occur following a reconfiguration of DC‐MGs, such as disconnection/reconnection of electrical sources, or rearrangement in the structure of DC‐MGs. In the DC‐MG under consideration, a number of the DC sources are implemented in the form of hybrid power conversion systems (HPCSs) consisting of a parallel combination of a super‐capacitor (SC), a fuel cell and a photovoltaic (PV) system. Due to the fast dynamics of the SC units, the stabilisation function of the DC‐MG during transients is performed by these units using a supplementary signal provided by the stabiliser. The proposed stabiliser is intended to operate in a decentralised manner. To this end, a novel Lyapunov function is proposed to adjust the stabiliser parameters based on local data relevant to each HPCS. The control scheme employed is based on a simple structure facilitating the implementation of the proposed stabiliser. Finally, time‐domain simulations are carried out demonstrating the effectiveness of the proposed control framework in a multisource DC‐MG.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".