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Record W2954345498

Voltage interactions and commutation failure phenomena in multi-infeed HVDC systems

2011· dissertation· en· W2954345498 on OpenAlexaboutno aff
Ebrahim Rahimi

Bibliographic record

VenueMspace (University of Manitoba) · 2011
Typedissertation
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsnot available
Fundersnot available
KeywordsCommutationVoltageElectrical engineeringEngineeringComputer science
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.202
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2011
Admission routes1
Has abstractyes

Explore more

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