Predictive Analysis for Radiated Electromagnetic Disturbance in MMC-HVDC Valve Hall
Bibliographic record
Abstract
The radiated electromagnetic interference (EMI) in the valve hall of the modular multilevel converter (MMC) station must be limited below a certain value to ensure the normal operation of the equipment and the safety of the engineers. In this paper, a method for predicting the radiated EMI is proposed, which takes all the necessary factors, such as the physics-based characteristics for IGBT module, sub-module topology, and converter space structure into consideration. This method involves the improved physical model of semiconductor devices, which discards the non-physical feedback parameter introduced by some previous research. At the same time, through the decoupling of the submodule and the bridge arm circuit, the order of the large-scale system is reduced so that the sub-modules can be calculated in parallel, greatly reducing the overall calculation burden of the model and accelerating the nonlinear small time-step simulation. The wideband characteristics of the converter valve tower are fully considered, with each of the bridge arms regarded as a two-port network with independent sources. The parameter integration and distribution process can also achieve complete parallel calculation. The converter valve tower is modeled as a complex antenna structure with the output voltage of each sub-module as the excitation, the calculation of near-field radiation intensity for the converter valve tower is performed in Alteir FEKO. The measurement conducted inside an actual 49-level converter station verifies the accuracy of the model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".