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Record W2024385644 · doi:10.1109/pes.2010.5589769

A hybrid network model for small signal stability analysis of power systems

2010· article· en· W2024385644 on OpenAlexaff
C. Karawita, U.D. Annakkage

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAdmittance parametersAdmittanceTransient (computer programming)Stability (learning theory)Control theory (sociology)SIGNAL (programming language)Time domainElectric power systemFrequency domainSmall-signal modelComputer scienceModal analysisModalConvertersElectronic engineeringConstant (computer programming)Transmission (telecommunications)Power (physics)EngineeringElectrical impedanceVoltagePhysicsFinite element methodElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

Summary form only given. Small signal stability analysis in a power system is typically concerned with electromechanical oscillations. For this purpose, it is adequate to model the transmission system using a constant admittance matrix. For torsional oscillations and HVDC interactions, the frequency of interest is much higher and the constant admittance representation is not sufficient. This paper proposes a hybrid model, which allows the parts of the transmission network in the vicinity of HVDC converters or any other dynamic devices to be modeled with their dynamics and the remaining parts to be modeled as constant admittances. The proposed hybrid methodology for small signal stability assessment is the main contribution of this paper. The proposed methodology is validated against an electromagnetic transient simulation program (PSCAD/EMTDC) using time responses. The proposed model and two other small signal models are compared against each other in the frequency domain using modal analysis.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.650
Threshold uncertainty score0.319

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 head0.017
GPT teacher head0.209
Teacher spread0.192 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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