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Record W2766127716 · doi:10.1109/isgt.2017.8086029

Analyzing subsynchronous torsional interactions in large-scale power systems in frequency domain

2017· article· en· W2766127716 on OpenAlexaff
Pouya Zadkhast, Frederic Howell, Xi Lin, Lei Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsPowertech Labs (Canada)
Fundersnot available
KeywordsCapacitorElectric power systemCompensation (psychology)Series (stratigraphy)Control theory (sociology)Computer scienceProcess (computing)Frequency domainMode (computer interface)Power (physics)EngineeringVoltagePhysicsElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a generic technique for analyzing subsynchronous torsional interaction (SSTI) between series capacitors and multi-mass shaft system in steam turbinegenerators. The proposed method focuses on application of the method in large-scale power systems where the SSTI study is to be carried out for large number of generators, different compensation levels, and various contingencies. The proposed methodology encompasses three steps: 1) Performing a network frequency scan to identify generators that are likely to show SSTI, also called critical generators; 2) Finding local mode of critical generators to identify critical torsional modes that are likely to interact with series capacitors; 3) Using frequency of critical modes as initial guess to trace them in the full system and find associated damping. This three-step procedure reduces computational cost while minimizing possibility of missing any torsional interaction in the system. Moreover, the developed procedure highly automates the process of finding subsynchronous modes and minimizes user interaction.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.643

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)0.0010.000

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 head0.010
GPT teacher head0.248
Teacher spread0.239 · 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 designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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