Computationally Efficient and Accurate Approach for Commutation Failure Risk Areas Identification in Multi-Infeed LCC-HVdc Systems
Bibliographic record
Abstract
Earlier approaches for commutation failure (CF) risk areas identification in multi-infeed LCC-HVdc systems include the simulation-based and analytical types. However, the former and latter will cause the low computational efficiency and inaccurate result, respectively. Thus, this article first clarifies CF performances caused by voltage depression and distortion in multi-infeed LCC-HVdc systems. Second, an ac-dc interaction factor (ADIF) index along with its analytical calculation method is proposed to quantify the voltage interaction between arbitrary ac line location and inverter bus. The ADIF is then used to develop a critical ADIF index for identifying the voltage depression induced CF. Third, a distortion ADIF index along with its mathematical expression is developed for identifying the voltage distortion induced CF. Fourth, combining the voltage depression and distortion induced CF identification methods, CF correlation regions where ac faults can induce CF in inverters are introduced to develop the proposed approach. Compared to earlier simulation-based approaches, the proposed approach is more efficient without recourse to simulations. Compared to earlier analytical approaches ignoring ac lines faults and the voltage distortion induced CF, the proposed approach is more accurate with these factors comprehensively considered. Finally, case study on an 8-infeed LCC-HVdc system validates the proposed approach.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".