MétaCan
Menu
Back to cohort
Record W2006968932 · doi:10.1109/pesmg.2013.6673048

New approach to damp subsynchronous resonance by reshaping the output impedance of voltage-sourced converters

2013· article· en· W2006968932 on OpenAlexaff
Khaled Alawasa, Yasser Abdel‐Rady I. Mohamed, Wilsun Xu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVoltage sourceAdmittanceControl theory (sociology)Electrical impedanceConvertersDamping factorResonance (particle physics)VoltageNegative impedance converterController (irrigation)Output impedanceGenerator (circuit theory)InterfacingEngineeringInput impedanceComputer sciencePhysicsPower (physics)Control (management)Electrical engineering

Abstract

fetched live from OpenAlex

This paper analyzes and investigates the effect of a voltage-source converter (VSC) on subsynchronous resonance and system damping interaction in a series-compensated system with multi-mass synchronous generator. The incremental output impedance/admittance of a VSC that is constructed by the circuit components and control parameters of a VSC is the key element for interactions with the grid. It has been shown that a VSC system might introduce negative damping (due to its negative resistance) that degrades the system damping from one side, and its reactive proprieties might affect (shift) the network resonance frequency and alter the damping prolife on the other side. An active damping controller is developed to reshape the output impedance of the interfacing VSC and minimize its associated negative impact. With little modification, the proposed damping technique is also able to damping the SSR, in a series-compensated system, as a new approach to damping SSR. Time-domain simulation results are presented to validate the theoretical analysis and show the effectiveness of the proposed active damping technique.

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 score0.969
Threshold uncertainty score0.379

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 head0.006
GPT teacher head0.169
Teacher spread0.163 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicMicrogrid Control and OptimizationFrench-language works237,207