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Three-Dimensional Fuzzy Logic Applied to DC Voltage Regulation in Active Power Filter of PV System

2023· article· en· W4384210856 on OpenAlexaff
F. Bourourou

Bibliographic record

VenueInternational Journal of Smart grid · 2023
Typearticle
Languageen
FieldEngineering
TopicEnergy Load and Power Forecasting
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsFuzzy logicTotal harmonic distortionControl theory (sociology)Computer scienceControl engineeringMATLABElectric power systemFuzzy electronicsVoltagePower (physics)Fuzzy control systemElectronic engineeringEngineeringControl (management)Neuro-fuzzyArtificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

This paper deals with the application of three-dimensional fuzzy logic to DC voltage regulation in active power filters exploration, the benefits of this approach, and examples of its application on power quality improvement of PV smart grid system installation. APF are important components of power systems that are used to minimize harmonic distortion and improve power quality. DC voltage regulation is a critical component of active power filters, and traditional control systems have limitations in their ability to account for complex and nuanced conditions, leading to less accurate control and less efficient use of resources. Three-dimensional fuzzy logic is an advanced approach to control systems that allows for more precise and nuanced evaluations of conditions, compering to classical fuzzy logic or the type 2, leading to more accurate control and more efficient use of resources, Three-dimensional fuzzy logic algorithm will be proposed and programed under MATLAB Simulink to control the APF and simulation results are represented and analysed.

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: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.403

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 head0.014
GPT teacher head0.228
Teacher spread0.213 · 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
Published2023
Admission routes1
Has abstractyes

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