An Overview of Modular Multilevel Converters in HVDC Transmission Systems With STATCOM Operation During Pole-to-Pole DC Short Circuits
Bibliographic record
Abstract
Fault-current handling capability of the modular multilevel converters (MMCs) under dc-cable short-circuit conditions is a major concern for the MMC applications on the high-voltage direct-current (HVDC) transmission systems, where the MMCs based on half-bridge submodules (SMs) cannot block the fault currents to protect the converter devices. In this paper, a comprehensive review for the fault-ride-through capability of the HVDC transmission systems based on the MMCs adopting different SM schemes is presented, where the MMCs can block the fault currents and compensate the reactive currents to the electric grid during the dc faults. An analysis of the dc short-circuit faults in the MMC is introduced and then the operation principle of different SM circuits building the MMC for blocking the fault currents is highlighted. The fault-tolerant operation of these MMC schemes as static synchronous compensator to enhance the ac grid stability during the dc faults is also investigated. A comparison in terms of investment cost, loss, volume, and controllability for various MMC topologies is performed. Comprehensive simulation results for the most promising topologies of the MMC with a capability of fault-ride through under dc-fault conditions are finally presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".