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Record W1562731717 · doi:10.1109/tdc.2006.1668604

Phase Selection and Directionality Issues when Protecting Lines with Series Compensation, HVDC Devices or Non-Traditional Generation

2006· article· en· W1562731717 on OpenAlexaff
B. Kasztenny, Dale Finney, Ilia Voloh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsGeneral Electric (Canada)
Fundersnot available
KeywordsTrippingCompensation (psychology)Maximum power transfer theoremComputer scienceCapacitorRelayElectric power transmissionSynchronization (alternating current)Electric power systemSeries (stratigraphy)Transmission systemElectronic engineeringPower (physics)Transmission (telecommunications)Reliability engineeringControl engineeringElectrical engineeringEngineeringCircuit breakerVoltageTelecommunications

Abstract

fetched live from OpenAlex

More and more often series compensation, non-standard generation and power electronic devices co-exist in a vicinity of EHV transmission lines. A typical scenario is a relatively long transmission line already with elements of HVDC and/or series-compensation, interconnected to a new wind farm with significant capacity driven by non-standard machines. In some cases single-pole tripping is applied in order to maximize power transfer during reclosing, and to avoid synchronization of the islanded generation. During transients, in such systems complex interactions occur between series capacitors, multiple machines, and HVDC devices. This includes both the primary equipment, and the associated controls. As a result the system responds differently compared to a traditional network fed from synchronous generators. Some protection techniques that are well-recognized and successfully applied in traditional configurations may perform inadequately under such complex conditions. The paper lists protection concepts that must be carefully examined or tested before using in such a difficult application environment. It also proposes practical supervision methods that could be implemented via custom logic on a modern multi-function relay in order to solve some of the performance problems

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.027
GPT teacher head0.252
Teacher spread0.226 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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