Decentralized Cooperative Control for Smart DC Home With DC Fault Handling Capability
Bibliographic record
Abstract
This paper presents a decentralized cooperative control (DCC) method along with a fault segment identification (FSI) scheme to achieve the control and protection objectives for a smart dc home by using only local measurements. The dc home is interfaced with the utility grid via a new modular multilevel converter configuration. Distributed generators are also integrated into the dc home via power converters to guarantee sufficient energy capacity and to support ac and dc loads consumption in the off-grid mode. The proposed DCC method ensures accurate current sharing, dc bus voltage regulation, and fast restoration after the fault clearance. On the other hand, the main objective of the proposed FSI scheme is to quickly identify and isolate the faulty segment to protect the sensitive power electronic components in the dc home from the high fault current. The FSI technique identifies the faulty segment by using only the information extracted from the local current sensor. Time-domain simulation studies using detailed nonlinear models confirm the effectiveness of the proposed control and protection schemes under various normal and faulted operating scenarios. Hardware-in-the-loop studies demonstrate the feasibility of hardware implementation and verify the proposed system performance.
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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) | 0.000 | 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) | 0.001 | 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".