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Record W2141416484 · doi:10.1109/pes.2006.1709613

Mitigation of subsynchronous oscillations in a series compensated wind farm with static var compensator

2006· article· en· W2141416484 on OpenAlexaff
Rajiv K. Varma, Sayantan Auddy

Bibliographic record

Venue2006 IEEE Power Engineering Society General Meeting · 2006
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsWestern University
Fundersnot available
KeywordsControl theory (sociology)Induction generatorStatic VAR compensatorController (irrigation)Wind powerElectric power systemEngineeringDoubly fed electric machineGenerator (circuit theory)Series (stratigraphy)Electric power transmissionAC powerVoltageControl engineeringPower (physics)Computer sciencePhysicsControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

Subsynchronous resonance (SSR) is a potential problem in a series compensated power system. A wind farm employing self-excited induction generator (SEIG) connected to the grid through a series compensated transmission line may experience the same problem. This paper presents a study of subsynchronous oscillations resulting from torsional interactions as well as induction generator self-excitation effects in such a wind energy conversion system (WECS). It is shown that these two phenomena may cause system instability if proper preventive measures are not taken. A static var compensator (SVC) with a voltage controller has been employed at the induction generator terminal to damp the subsynchronous oscillations. It is also found that an auxiliary subsynchronous damping controller (SSDC) improves the damping of torsional oscillations. Extensive time domain simulations have been carried out using EMTDC/PSCAD to validate the performance of the SVC in preventing SSR

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)0.000
metaresearch head score (Gemma)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.003
GPT teacher head0.176
Teacher spread0.172 · 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

Citations41
Published2006
Admission routes1
Has abstractyes

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