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Record W3030577229 · doi:10.18280/ejee.220212

Power Conditioning Using DVR under Symmetrical and Unsymmetrical Fault Conditions

2020· article· en· W3030577229 on OpenAlex
Marshall Philip, Peer Fathima Abdul Kareem

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEuropean Journal of Electrical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsnot available
Fundersnot available
KeywordsConditioningPower (physics)Fault (geology)Reliability engineeringElectrical engineeringEngineeringPhysicsGeologyMathematicsThermodynamicsSeismologyStatistics

Abstract

fetched live from OpenAlex

Voltage sags are the major power quality problems and the Dynamic Voltage Restorer (DVR) is considered as an effectual custom power device to attenuate voltage sags. Based on the genesis of voltage sag, there are two types of faults occurring in electrical power distribution systems such as symmetrical and unsymmetrical faults. This paper describes about the various types of voltage sags, Current, Real and Reactive Power in a distribution system, and a brief analysis on pre-fault, during fault and post fault conditions. Various system indices such as Sag Score, Voltage sag energy index, Voltage Sag Lost Energy index, Voltage Sag Severity and Phase Voltage Unbalance Rate have been calculated and analyzed. Also the performance analysis of Total Harmonic Distortion (THD) and Power Factor in distribution system is carried out under fault conditions. The simulation results are carried out in Matlab Simulink environment.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.509
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
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.205
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