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Record W4312973683 · doi:10.35833/mpce.2021.000336

Improved Synergetic Current Control for Grid-connected Microgrids and Distributed Generation Systems

2022· article· en· W4312973683 on OpenAlex

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

VenueJournal of Modern Power Systems and Clean Energy · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsQueen's UniversityRoyal Military College of Canada
Fundersnot available
KeywordsControl theory (sociology)Integral sliding modeMicrogridController (irrigation)Overshoot (microwave communication)Convergence (economics)InverterDSPACEGridComputer scienceControl engineeringControl (management)EngineeringVoltageSliding mode controlMathematicsAlgorithmNonlinear systemElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents the development of improved synergetic current control for the injected current of an inverter in the grid-connected microgrid and the distributed generation system (DGS). This paper introduces new formulas of the macro-variable functions for integral synergetic control (SC) and integral fast terminal SC, which both have an integral term to guarantee zero steady-state error. The proposed integral SC and integral fast terminal SC achieve a seamless performance such as the fast convergence, minimal overshoot, zero steady-state error, and chattering-free operation. To demonstrate the meritorious performance of the proposed scheme for injected current control, it is compared with the performance of a proportional-integral (PI) controller and advanced exponential sliding mode control (SMC). Finally, the practicality of the proposed scheme is justified by experimental results obtained through rapid control prototyping (RCP) using the dSPACE-SCALEXIO platform.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score0.652

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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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