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Artificial Intelligent Control of Compact Multilevel Converters in Grid Forming and Grid Following Modes without Islanding Detection

2023· article· en· W4388736927 on OpenAlex
Mohammad Babaie, Kamal Al‐Haddad

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsIslandingController (irrigation)ConvertersComputer scienceGridSupervisorArtificial neural networkControl theory (sociology)Power (physics)Electronic engineeringControl engineeringEngineeringControl (management)Distributed generationArtificial intelligenceElectrical engineeringVoltageRenewable energy

Abstract

fetched live from OpenAlex

This paper develops a robust artificial intelligent controller for a compact multilevel converter to operate in grid-following and grid-forming modes without islanding detection. The proposed controller is designed using sliding mode control, a multilayer Perceptron neural network, and a unique supervisor to select the operation mode of the converter automatically. Compared with islanding detection-based control methods, a single unit controller is required, which is more robust against false trips. The simulation tests also confirm that this technique is highly accurate and only causes a non-detection zone when the load demand is equal to the peak power of the converter.

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 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 score0.607
Threshold uncertainty score0.647

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 head0.019
GPT teacher head0.243
Teacher spread0.224 · 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

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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