Machine Learning Based Modeling for Real-Time Inferencer-in-the-Loop Hardware Emulation of High-Speed Rail Microgrid
Bibliographic record
Abstract
The application of artificial intelligence (AI) technology in the field of power systems and power electronic devices is increasingly prevalent. With massive datasets generated by a wide range of equipment, AI-based modeling is promising in the future of hardware-in-the-loop emulation. This article studies and improves the machine learning based modeling approach for power electronic devices, and the inferencer-in-the-loop (IIL) system is proposed together with optimized neural network (NN) models. The high-speed rail microgrid, includes autotransformer rectifier unit subsystems, energy storage subsystems, two-level converter based permanent magnet synchronous motor propulsion subsystems, and modular multilevel converter based induction motor propulsion subsystems, serves as study cases to demonstrate the adaptability of this approach. Finally, to show high accuracy and versatility of the IIL real-time emulation system, the system-level (1 timestep) and device-level (50 timestep) results are compared in three domains: the referencer system (C code simulation program in NVIDIA Jetson and offline SaberRD datasets), offline inferencer emulation on Xilinx VCU118 board, and online refined inferencer emulation on Xilinx VCU118 board.
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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".