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Record W3037236726 · doi:10.1109/tpwrs.2020.3004906

Analytically Validated SSCI Assessment Technique for Wind Parks in Series Compensated Grids

2020· article· en· W3037236726 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Power Systems · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsHydro-QuébecPolytechnique Montréal
Fundersnot available
KeywordsAdmittanceWind powerTransient (computer programming)InterconnectionComputer scienceGridReliability engineeringStability (learning theory)Electric power systemTurbineField (mathematics)Control engineeringPower (physics)EngineeringElectrical impedanceMathematicsElectrical engineeringTelecommunicationsMachine learningAerospace engineering

Abstract

fetched live from OpenAlex

This work highlights limitations in existing screening approaches to assess low-frequency interactions during interconnection studies of grid-connected inverters (GCIs), such as wind power plants. It then proposes a new screening methodology capable of addressing these limitations for realistic representations of power system components and GCIs. It demonstrates that the asymmetric input admittance characteristics of GCIs, whose consideration is required for proper stability assessment, can be accurately extracted with the proposed adequate scanning method implemented in an electromagnetic transient (EMT) software package. This is verified through rigorous validation against analytical formulations developed for field validated type-III and type-IV wind turbine models. It shows that the technique is capable of correctly assessing stability in all presented cases. All results are validated against detailed EMT simulations.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.770

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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