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Record W2134534853 · doi:10.1109/pes.2008.4595991

Support tools for simulation-based optimal design of power networks with embedded power electronics

2008· article· en· W2134534853 on OpenAlexaff
M. Heidari, Shaahin Filizadeh, A.M. Gole

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsSensitivity (control systems)Pareto principleTransient (computer programming)Multi-objective optimizationComputer scienceCapacitorPower electronicsPlot (graphics)Optimal designPower (physics)HarmonicElectronic engineeringNonlinear systemControl theory (sociology)VoltageMathematical optimizationEngineeringControl (management)MathematicsElectrical engineering

Abstract

fetched live from OpenAlex

Summary form only given. The paper presents a gradient-based nonlinear optimization wrapper for an electromagnetic transient simulation program to assists in the design of hardware and control parameters of FACTS apparatus. A new second-order sensitivity analysis method is introduced that quantifies the sensitivity of the optimal solution to parameter uncertainties. Using this approach, the sensitivity of the harmonic elimination capability of a pulse-width modulated voltage source converter to variations in switching angles is investigated. The paper also adapts the method of Pareto optimization to the simulation-based design of FACTS apparatus when multiple design objectives are to be satisfied. The developed Pareto optimization tool generates a Pareto frontier plot to visualize the design tradeoffs between the multiple objectives. The effectiveness of the tool is demonstrated through a design example that selects optimal values for the dc-bus capacitor and the control settings of a STATCOM.

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 score0.976
Threshold uncertainty score0.577

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 head0.229
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

Citations2
Published2008
Admission routes1
Has abstractyes

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