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Record W2157447140 · doi:10.1109/iciinfs.2013.6731945

A case study to demonstrate the benefit of using synchrophasor signals for power system stabilizers

2013· article· en· W2157447140 on OpenAlexafffund
A. I. Konara, U.D. Annakkage

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Manitoba
FundersManitoba HydroUniversity of Manitoba
KeywordsElectric power systemControl theory (sociology)Stabilizer (aeronautics)SIGNAL (programming language)Controller (irrigation)Power (physics)EngineeringComputer scienceSensitivity (control systems)Control signalControl engineeringControl systemElectronic engineeringControl (management)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a case where the design of a Power System Stabilizer (PSS) to achieve desired damping using local speed feedback signals is difficult using the conventional design technique. It is shown that synchrophasor based two input power system stabilizer can be designed without much difficulty to achieve the desired damping. Eigenanalysis is performed on the reduced equivalent of New England New York power system. One controller location was found as the best location to improve damping of three inter-area modes. Residue analysis reveals the presence of a remote signal which has better sensitivity to the same controller location. The procedure of designing a two input PSS that uses a local signal and a remote signal is presented and it is shown that the required performance is achieved.

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)0.002
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.252
Teacher spread0.227 · 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

Citations2
Published2013
Admission routes2
Has abstractyes

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