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Record W2626223255 · doi:10.1109/icps.2017.7945130

Disturbance-estimator predictive current controller for 1φ interconnected PV systems

2017· article· en· W2626223255 on OpenAlexaff
H. Mohomad, S. A. Saleh, Liuchen Chang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)Photovoltaic systemEstimatorSensitivity (control systems)GridComputer scienceDisturbance (geology)Current (fluid)Open-loop controllerEngineeringControl engineeringElectronic engineeringMathematicsControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

This paper presents the analysis, development, and experimental performance of a predictive current controller that is featured with a disturbance estimator. The developed current controller is designed to operate a single phase (1φ) grid-side power electronic converter (PEC) employed in photovoltaic (PV) systems. The disturbance estimator is used to reduce the controller sensitivity to parameter variations, as well as to reject grid side disturbances. The design of the controller and its disturbance estimator are carried using the pole-placement method. The performance of the developed current controller is experimentally tested for a 5.4 kW interconnected PV system under different levels of power delivery to the grid that are accompanied with different variations in system parameters. In addition, other controllers used for interconnected PV systems are tested to highlight the advantages of the developed current controller. Performance and comparison results show accurate, fast, and robust responses that are initiated with negligible sensitivity to parameters variations and disturbances on the grid side.

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: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.450

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 head0.234
Teacher spread0.223 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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