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Record W1969839574 · doi:10.1109/pesmg.2013.6672446

Impacts of variable quadrature reactance on power system stabilizer performance

2013· article· en· W1969839574 on OpenAlexafffund
Hansong Su, Ravi Mutukutti, David C. Apps

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsBC Hydro (Canada)
FundersBC Hydro
KeywordsControl theory (sociology)ReactanceExciterPermanent magnet synchronous generatorMode (computer interface)Computer scienceQuadrature (astronomy)Electric power systemVariable (mathematics)Rotor (electric)Synchronous motorPower (physics)VoltageMathematicsEngineeringElectronic engineeringPhysicsAcousticsControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

In the absence of rotor speed as a measured variable with acceptable resolution and accuracy, exciter manufacturers drive speed from other measurements. A common method used in the dual input PSS2A and PSS2B stabilizers is to derive the speed deviation Δω input by using generator internal voltage behind quadrature reactance Xq. The Xq of a synchronous machine deviates from the synchronous value when rotor speed oscillates during a disturbance, which is not always considered or analyzed in adequate depth in the Power System Stabilizer (PSS) time domain simulations during PSS planning, tuning and validation with respect to local mode and inter-area mode oscillations. Inappropriate Xq setting without considering its variations may result in underperformance of the PSS. This paper discusses the impact of the variable Xq on the PSS performance at local mode and inter-area mode. An appropriate Xq setting with emphasis in damping inter-area mode oscillations and a set of ramp tracking filter settings with low gain at local mode were recommended in a case study to ensure satisfactory PSS performance in both local mode and inter-area mode.

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 categoriesInsufficient payload (model declined to judge)
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 score0.281
Threshold uncertainty score1.000

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 head0.004
GPT teacher head0.169
Teacher spread0.165 · 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.

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

Citations3
Published2013
Admission routes2
Has abstractyes

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