MétaCan
Menu
Back to cohort
Record W1964904240 · doi:10.1109/pesmg.2013.6672685

Damping low-frequency oscillations by tuning the operating point of a dc-segmented ac system

2013· article· en· W1964904240 on OpenAlexaff
Sahar Pirooz Azad, Reza Iravani, Joseph Euzebe Tate

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOperating pointControl theory (sociology)Electric power systemLow-frequency oscillationElectric power transmissionOscillation (cell signaling)Power (physics)Transmission systemStability (learning theory)Point (geometry)Flexibility (engineering)Transmission (telecommunications)Computer scienceEngineeringElectronic engineeringPhysicsElectrical engineeringMathematicsControl (management)

Abstract

fetched live from OpenAlex

This paper introduces and evaluates a new method for the small-signal stability enhancement of large ac power systems based on segmentation through line-commutated HVDC links. The proposed method is based on the fact that the oscillatory modes of the system may vary by changing the system operating point. In this study, the system operating point is varied by rerouting the flow of power in the ac transmission lines. The flexibility of the HVDC lines in controlling the flow of power in a dc-segmented ac system is used to control the power transmission in the ac lines. An optimization problem is proposed to determine the optimum set-points of the HVDC lines to increase the damping ratio of the underdamped oscillatory modes. Simulation results show that at the optimum operating point, the low-frequency oscillatory modes are damped out significantly and the system net oscillation is less than that of the system at the operating point obtained from a standard economic dispatch. Simulation results also show that there is a trade-off between the generation cost and damping ratio and the proposed method effectively enhances the small-signal stability for a variety of system configurations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.192
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicHVDC Systems and Fault ProtectionFrench-language works237,207