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Record W2980264344 · doi:10.1109/ccece.2019.8861801

Extended Modulus Optimum Method for Off-Grid Inverter’s Voltage Control System

2019· article· en· W2980264344 on OpenAlexaff
Nikolay Radimov, Shichao Liu, Xiaoyu Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsInverterStationary Reference FrameControl theory (sociology)GridComputer scienceReference frameFrame (networking)Controller (irrigation)Transient (computer programming)Transfer functionHarmonicSteady state (chemistry)Frequency gridBandwidth (computing)VoltageRepetitive controlControl systemEngineeringControl (management)Electrical engineeringMathematicsInduction motorPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Stationary reference frame proportional-resonant (PR) controllers play a significant role in grid-tie and off-grid inverter control because of their ability to achieve zero steady-state error at AC frequency and provide high rejection ratio for undesirable harmonic content with minimum computation burden. While extensive efforts have been put into application-oriented performance optimization for grid-tie application, the off-grid inverter case was barely investigated. This paper proposes an extension of the Modulus Optimum tuning method from a synchronous frame to a stationary frame. The proposed approach is directly applied to the transfer function of the off-grid inverter in a stationary frame. As a result, the structure of the controller with optimized transient and steady-state behaviours is synthesized. The relationship between the proportional and the resonant part of the investigated controller is obtained to achieve the optimum utilization of the presented control bandwidth. The validation of the presented approach was performed by computer simulations and laboratory experiments on the off-grid battery inverter.

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 score0.892
Threshold uncertainty score0.648

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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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