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Record W2037034085 · doi:10.2202/1553-779x.1607

Damping of Inter-Area Oscillation in Power Systems by Static Var Compensator (SVC) Using PMU-Acquired Remote Bus Voltage Angles

2007· article· en· W2037034085 on OpenAlexaff
Rajiv K. Varma, Rajesh Gupta, Soubhik Auddy

Bibliographic record

VenueInternational Journal of Emerging Electric Power Systems · 2007
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsWestern University
Fundersnot available
KeywordsPhasorStatic VAR compensatorElectric power systemControl theory (sociology)Robustness (evolution)VoltageSIGNAL (programming language)Transmission (telecommunications)AC powerComputer scienceElectronic engineeringEngineeringPower (physics)Electrical engineeringControl (management)

Abstract

fetched live from OpenAlex

This paper presents a novel concept of Static Var Compensator (SVC) damping control using a weighted sum of bus voltage angles of remote generators that are responsible for causing inter-area oscillations in power systems. These remote bus voltage angle signals are acquired through Phasor Measurement Units (PMUs). A time domain simulation study on a 39-bus New England multi-machine system is utilized to show that an SVC auxiliary damping control, based on the derivative of these remote bus voltage angles, is superior to the conventionally employed local signals in damping inter-area oscillations. The effect of transmission delay in the acquisition of remote signals is also presented and a simple mechanism to compensate this delay is proposed and validated. The robustness of the delay compensator is finally shown for a wide range of signal transmission delays. The designed SVC controller shows a superior performance in damping the inter-area oscillations despite the signal transmission delays.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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Same venueInternational Journal of Emerging Electric Power SystemsSame topicPower System Optimization and StabilityFrench-language works237,207