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Record W1964831186 · doi:10.1109/pesgm.2014.6939085

Out-of-step protection using state plane trajectories analysis

2014· article· en· W1964831186 on OpenAlexaff
Binod Shrestha, Ramakrishna Gokaraju, Mohindar S. Sachdev

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsElectric power systemTransient (computer programming)Control theory (sociology)Fault (geology)Energy (signal processing)State (computer science)Computer sciencePlane (geometry)Power (physics)Permanent magnet synchronous generatorAlgorithmMathematicsGeometryArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Summary form only given. This paper proposes a novel out-of-step protection technique using the state-plane representation of the generator speed and power angle. The critical clearing angle is computed using the principle that the total energy of the system at the instant the fault is cleared should be equal to the maximum potential energy of the system. The critical clearing time corresponding to the value of critical clearing angle is obtained directly using the time calibration of the relative speed versus power angle solution curve. The simultaneous calculation of the critical clearing angle and the time makes the proposed approach much faster than the two-blinder scheme. The proposed state plane prediction scheme is used to detect the first swing out-of-step condition in a two-area test system using system wide information. The two generators are represented with a single machine infinite bus (SMIB) equivalent system, and the state plane algorithm is applied to the reduced equivalent. Electromagnetic transient simulations are carried out using PSCAD/EMTDC to test the proposed algorithm in the two-area test systems. The simulation studies show that the proposed method is computationally efficient, and accurate. The technique also does not require any offline studies.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.654
Threshold uncertainty score0.368

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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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 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

Citations4
Published2014
Admission routes1
Has abstractyes

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