Voltage interactions and commutation failure phenomena in multi-infeed HVDC systems
Bibliographic record
Abstract
This research attempts to quantify the complex interactions between HVDC transmission schemes in a multi-infeed configuration, particularly with regard to the voltage interactions and the commutation failure phenomena. The in-depth analysis of multi-infeed HVDC systems discussed in this research shows the application of several indices such as the MIIF, MIESCR, and CFII, that can provide researchers and planning engineers in the area of HVDC transmission with the necessary tools for their system studies. It shows that these indices are applicable in a multi-infeed system comprising HVDC schemes with different ratings. The Multi-Infeed Interaction Factor (MIIF) quantifies the level of voltage interactions between converter ac buses. The Multi-Infeed Effective Short Circuit Ratio (MIESCR) index is an indicator of ac system strengths with regard to the assessment of the transient overvoltage (TOV) and the power-voltage stability of multi-infeed HVDC systems. The Commutation Failure Immunity Index (CFII) utilizes electromagnetic transient simulation programs to evaluate the immunity of an HVDC converter to commutation failures. The CFII takes into account the ac system strength and the HVDC controls and evaluates their impact on the commutation process. The immunity of both single-infeed and multi-infeed systems to commutation failure phenomena are accurately evaluated and quantified by the CFII. Using the CFII, it is shown that the current commutation in multi-infeed HVDC schemes could fail under circumstances in which the probability of failure had been perceived to be low. The causes of, the effects of, and the remedial actions needed to deal with such anomalous commutation failures are discussed in this thesis. The capability of the new indices to provide an insight into the interactions phenomena in multi-infeed systems are clearly demonstrated by examples that show their application in the analysis of an actual multi-infeed HVDC system that is in the planning phase in the province of Alberta in Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".