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Record W4319755422 · doi:10.3390/electronics12040860

Synchronization and Control of a Single-Phase Grid-Tied Inverter under Harmonic Distortion

2023· article· en· W4319755422 on OpenAlexaff
Kamyar Seifi, Mehrdad Moallem

Bibliographic record

VenueElectronics · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHarmonicsTotal harmonic distortionController (irrigation)InverterControl theory (sociology)Phase-locked loopHarmonicElectronic engineeringSynchronization (alternating current)GridComputer scienceDistortion (music)Three-phaseEngineeringVoltageElectrical engineeringTopology (electrical circuits)Control (management)PhysicsPhase noiseMathematics

Abstract

fetched live from OpenAlex

Grid-connected inverters in renewable energy systems must provide high-quality power to the grid according to regulatory standards such as the IEEE 1547. To provide high-quality current control when the inverter is connected to a distorted grid, the frequency and phase information of the fundamental harmonic of the grid should be accurately obtained. This paper examines controller design for a single-phase inverter when there is distortion in the grid voltage. The control structure is designed to enhance the quality of the injected current into the grid. To this end, a frequency-locked loop (FLL) sinusoidal tracking controller which is able to reject the grid harmonics is proposed. Thus, the contribution of this paper is a new frequency-locked loop structure with adaptive notch filters that can provide accurate estimation of grid phase and frequency and improve the performance of single-phase inverters working under harmonic distortion. We also present an explanation of how the proposed adaptive nonlinear scheme can be discretized for digital implementation on a microcontroller. Experimental and simulation results are presented to demonstrate the performance of the proposed controller in eliminating distortion and enhancing the quality of the produced power.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.671
Threshold uncertainty score0.413

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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