A PC-based hardware-in-the-loop simulator for the integration testing of modern train and ship propulsion systems
Bibliographic record
Abstract
Today, the development and integration of train and ship controllers is a more difficult task than ever. Emergence of high-power switching devices has enabled the development of new solutions with improved controllability and efficiency. It has also increased the necessity for more stringent test and integration capabilities since these new topologies come with less design experience on the part of the system designers. To address this issue, a real-time simulator can be a very useful tool to test, validate and integrate the various subsystems of modern rail vehicle devices. This paper presents such a real-time simulator, based on commercial-off-the-shelf PC technology, suitable for the simulation of train and ship propulsion devices. The requirements for rail/water vehicle test and integration reaches several levels on the control hierarchy from low-level power electronic converters used for propulsion and auxiliary systems to high-level supervisory controls. This paper places great emphasis on the real-time simulation of several high-power drives used for train and ship propulsion, including a multi-induction machine drive, a three-level GTO - PMSM drive and a high-power thyristor-based converter - synchronous machine drive. All models are designed first with the SimPowerSystems blockset and then automatically compiled and run on commercial PCs under RT-LAB. Interfaces to I/O are also made at the Simulink model level without any low-level coding required by the user. Supervisory control integration and testing can also be made using the RT-LAB real-time simulator. The other objective of this paper is to demonstrate that HIL testing of complex drives, such as the those found on trains, can be done using commercial-off-the-shelf (COTS) software and hardware and model-based design techniques that only require high-level system models suitable for system specifications down to controller test and final system integration.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".