An Equivalent Circuit Method for Modelling and Simulation of Modular Multilevel Converters in Real-Time HIL Test Bench
Bibliographic record
Abstract
In China, two modular multilevel converter (MMC) projects were recently commissioned, and another two are currently under construction. The real-time hardware-in-the-loop (HIL) test bench played an important role in validating the manufacturer's controllers. The full detail MMC model does not fit with the HIL test bench, which has to solve the circuit containing numerous switches and handles a large amount of inputs and outputs at a small time step for real-time simulation. The main challenge is to find a method to model and simulate the MMC systems with sufficient detail, accuracy, and speed. This paper presents an equivalent circuit method for the HIL test bench. The circuit inside the submodule is represented by mathematical equations, implemented in CPU or a field-programmable gate array, and solved in parallel to achieve real-time performance. This method-based test bench is used in those MMC projects in China and connected to the manufacturer's controllers for HIL tests. The model accuracy and simulation speed achieved by this method met the requirements of the HIL tests. In this paper, various scenarios are tested in an MMC HVDC study system. The results achieved by the proposed method have high agreement with those of a reference model in EMTP-RV.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".