Correcting the Calculation Method of Commutation Failure Immunity Index for LCC-HVDC Inverters
Bibliographic record
Abstract
The commutation failure immunity index (CFII) recommended by CIGRE is an useful indicator for quantifying the immunity of LCC-HVDC inverters to the CF. It should be calculated by the electromagnetic transient (EMT) simulations under the worst fault condition that doesn't cause the CF as per its definition. Moreover, the symmetrical three-phase line-to-ground fault has been empirically claimed by CIGRE as the worst one in the previous calculation method. However in this letter, it is firstly found that the unsymmetrical double-phase fault rather than the three-phase one is actually the worst as clearly observed from large numbers of the EMT simulations. This is achieved by the comprehensive analysis of the simulated CF characteristics when taking into account the diversified fault conditions such as fault type, property, severity, initiation time. Secondly, the above interesting finding motivates a corrected CFII calculation method to be proposed considering the double-phase fault. The proposed method is further shown to be more superior than the previous method by the case studies under various ac grid strength.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".