Cable Surge Arrester Operation Due to Transient Overvoltages Under DC-Side Faults in the MMC–HVDC Link
Bibliographic record
Abstract
The dc cables of the voltage-sourced converter-based HVDC link, including the modular multilevel converter (MMC) configuration, must be protected by surge arresters against transient overvoltages. The selection of the appropriate surge arresters requires a comprehensive understanding of the phenomena that cause the overvoltages and accurate calculation of energy discharged in the arresters. This paper investigates the mechanism that causes transient overvoltages, due to dc-side line-to-ground faults, in a cable-connected MMC-HVDC link. This paper also evaluates the impacts of various system parameters, for example, the prefault power transfer level, the MMC arm inductance, the transformer leakage inductance, the fault location, the ac system short-circuit capacity, the cable inductance, and the MMC blocking delay, on the peak transient overvoltages and the amount of energy discharge in the cable surge arresters. The transient overvoltage studies are also conducted when the dc cable is replaced by an overhead line. The studies are performed in the PSCAD platform. The studies conclude that the prefault power transfer level and the MMC blocking delay have the most significant impacts on the surge arrester energy discharge under dc-side line-to-ground faults in the MMC-HVDC link. The study results also provide essential information for the design of the MMC-HVDC link components.
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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 | 0.000 |
| 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) | 0.001 | 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".