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Record W2346051546 · doi:10.1049/iet-gtd.2015.0764

Screening technique for identifying the risk of sub‐synchronous resonance

2016· article· en· W2346051546 on OpenAlexaff
Haifeng Li, Chongru Liu, Gengyin Li, U.D. Annakkage

Bibliographic record

VenueIET Generation Transmission & Distribution · 2016
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of Manitoba
FundersProgram for New Century Excellent Talents in UniversityHigher Education Discipline Innovation ProjectNational Natural Science Foundation of China
KeywordsComputer scienceResonance (particle physics)PhysicsAtomic physics

Abstract

fetched live from OpenAlex

A screening method called the compensation impact factor (CIF) is proposed in this study to identify the risk of sub‐synchronous resonance (SSR) caused when generators are connected to series compensated transmission lines. First, the definition of CIF is given and the meaning behind this index is explained. Second, the electric damping is used to classify the operating scenario into high SSR risk group and low SSR risk group. Then the correlation between the value of the CIF and the risk classification is analysed and the threshold value of CIF for this classification is calculated by using conditional probability. The proposed method is validated by (a) a 3‐bus system with one generator and (b) the IEEE‐39‐bus system with ten generators. The advantage of applying the proposed method is that only the high‐risk scenarios need to be analysed in detail using electromagnetic transient simulation.

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)0.004
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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.242
Teacher spread0.224 · 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
GenreMethods

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

Citations6
Published2016
Admission routes1
Has abstractyes

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