Control Architecture of Resilient Interconnected Microgrids (RIMGs) For Railway Infrastructures
Bibliographic record
Abstract
This chapter aims at designing Resilient Interconnected Microgrids (RIMGs) for railway infrastructures to achieve economical and high-performance energy supply. The proposed RIMGs include AC/DC Distributed Generation (DG) technologies as well as advanced energy storage, which is Flywheel Energy Storage Platform (FESP). FESP will provide dynamic energy storage to balance railway loads and generation capacities from RIMGs and the grid to achieve high performance and economical operation. Microgrid Supervisory Controller (MGSC) is proposed to effectively manage the power flow and to determine the most suitable energy sources in view of the railway demands within each MG and will be able to coordinate with other MGSCs for linked MGs, as well as to interact with the central controller. The proposed control architecture will enable the optimization of power supply cost to the train, as well as between MGs and the utility grid. The proposed solution has been demonstrated using simulation and real data, where the achieved results confirm the effectiveness of the proposed control architecture in terms of cost and performance.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".