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Mitigating Islanded Mode Small Scale Synchronous Generator Mechanical Oscillations Caused by Electrical Arc Furnace

2020· article· en· W3126498718 on OpenAlexaff
Yashar Kor, Mahdi Davarpanah, Reza Bekhradian, Majid Sanaye‐Pasand

Bibliographic record

Venue2020 IEEE Electric Power and Energy Conference (EPEC) · 2020
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectric arc furnaceGenerator (circuit theory)Mode (computer interface)Scale (ratio)Electric arcArc (geometry)Permanent magnet synchronous generatorElectrical engineeringAutomotive engineeringMaterials scienceComputer scienceMechanical engineeringPower (physics)EngineeringVoltageElectrodeMetallurgyPhysics

Abstract

fetched live from OpenAlex

Electrical arc furnace (EAF) is a nonlinear and time-varying load consuming low frequency fluctuating power. This results in mechanical fatigue in the nearby small scale synchronous generators (SSSGs), especially their corresponding turbines, that reduces their lifetime. Furthermore, to improve the power supply reliability and reduce electricity expenditures, utilizing SSSGs is inevitable. In this paper, an industrial EAF is modelled and verified based on the field measurement studies; moreover, an actual islanded microgrid including an EAF and an SSSG is studied to quantize the effect of EAF fluctuations on the SSSG mechanical parameters based on the introduced appropriate indices. In addition, the SSSG controller parameters are properly determined to alleviate the mechanical torque fluctuations. The use of the proposed practical and simple method without imposing any additional cost decreases the SSSG mechanical oscillation magnitude considerably.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.195
Teacher spread0.186 · 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.

Study designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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