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Record W2106549164 · doi:10.1109/icit.2011.5754343

Adaptive DC bus voltage control for renewable energy production systems with uncertainties

2011· article· en· W2106549164 on OpenAlexaff
Hicham Chaoui, Pierre Sicard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsSupercapacitorConvertersRenewable energyVoltageAdaptive controlComputer scienceEnergy storageControl theory (sociology)Control (management)Electronic engineeringEngineeringCapacitancePower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

This paper introduces an adaptive DC bus voltage control strategy for renewable energy systems, combining different energy storage devices, i.e., batteries, fuel cells, and supercapacitors. Multiple bidirectional DC-DC converters accurate control is achieved with no source voltage sensing, which reduces the number of sensors. Unlike many adaptive controllers, the proposed strategy uses a single adaptive parameter, which yields reduced complexity. The proposed approach is compared to a classical PI-based cascaded control scheme. The adaptive control strategy achieves better performance, which yields a high-efficiency operation of the overall system.

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: Methods · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.371

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 head0.011
GPT teacher head0.156
Teacher spread0.146 · 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
GenreMethods

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

Citations1
Published2011
Admission routes1
Has abstractyes

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