An Accurate and Fast Method for Conducted EMI Modeling and Simulation of MMC-Based HVdc Converter Station
Bibliographic record
Abstract
The analysis of electromagnetic interference (EMI) noise of power electronic circuits involves both the transient characteristics of power semiconductor devices and the wideband stray parameters of passive equipment. Modular multilevel converters (MMCs) used in high-voltage direct current (HVdc) transmission systems contain thousands of submodules (SMs), which makes it considerably challenging to perform device-level simulation on the traditional commercial software. This article presents an accurate and fast method for wideband modeling and simulation of MMC-HVdc system for the assessment of conducted EMI during the design stage. Physical characteristics of the semiconductor devices, parasitic parameters of the insulated-gate bipolar transistor (IGBT) packages, and stray capacitances of the SMs are all taken into consideration, and massively parallel transient simulation of the wideband MMC model is carried out on the graphics processor (GPU). The accuracy and efficiency of the GPU-based parallel algorithm are validated by the comparison with the experimental measurement of an 11-level full-bridge MMC prototype. Furthermore, the stray capacitance network of the valve tower in HVdc project is extracted, and a matrix partition method based on the shielding plate configuration is utilized to conduct the computation in a fully parallelized manner. The developed GPU program is used to run the large-scale case of a 201-level two-terminal MMC-HVdc system, and the primarily affected frequency range by various factors is analyzed. Execution time test is conducted for different level topology, and it is demonstrated that the GPU can achieve a remarkable speedup over multicore CPUs, especially when the system scale is more substantial.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".