Hybrid ML-EMT-Based Digital Twin for Device-Level HIL Real-Time Emulation of Ship-Board Microgrid on FPGA
Bibliographic record
Abstract
Maritime industries desire high speed and reliability, low lifespan cost, and environmental impact shipping for transportation. Compared to highly congested land shipments and high-cost air freight, all-electric ship (AES) can reduce the lifespan energy consumption and transport a considerable freight volume at a lower rate. Recently, the medium voltage dc (MVDC) topology, recommended by IEEE standard, pushes the AES to the next stage in considering space and weight constraints with the reduction of bulky transformers and simplified parallel connections. However, device-level modeling of this massive parallel MVDC-based ship-board microgrid (SBM) is challenging to both the state-of-the-art general-purpose compute unit and traditional electromagnetic transient (EMT)-based emulation. With the rapid development of machine learning (ML) algorithm and its dedicated execution unit, accelerated parallel emulation becomes achievable in different levels of this paralleled connected SBM. Applying the ML-aided technique can help to improve the emulation execution efficiency and reduce the consumption of hardware resource on the field-programmable gate arrays. This work proposes a real-time hybrid ML-EMT-based digital twin of the complete SBM at the subsystem-level and equipment-level with validated results from PSCAD/EMTDC, and device-level with validated results from SaberRD.
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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) | 0.000 | 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 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".