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Fractional PR Control of a Grid Tied Flying Capacitor Inverter for PV Applications

2019· article· en· W2930310024 on OpenAlexaff
Jaber Fallah Ardashir, Amin Mohammadpour Shotorbani, Hossein Khounjahan, Mehran Sabahi

Bibliographic record

VenueJournal of Energy Management and Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsControl theory (sociology)InverterMaximum power point trackingCapacitorPhotovoltaic systemGridRippleController (irrigation)TransformerEngineeringComputer scienceVoltageElectrical engineeringControl (management)Mathematics

Abstract

fetched live from OpenAlex

This paper presents a single-phase transformer-less flying capacitor inverter (FCI) for applications in grid-tied PV systems. In this structure, the neutral point of the grid is connected directly to the negative terminal of PV, so the common mode (CM) leakage current eliminate without using any control strategy. The main merits of this inverter are flexible grounding configuration and high safety. A fractional-order proportional resonant (FPR) controller is employed for the proposed FCI to improve its performance during transient and steady state operation. The low output current ripple of the proposed FPR controller against parameter uncertainties is compared with conventional proportional resonant (PR) controller. The capability of controllers is investigated for parametric changes. The grid connected inverter can achieve the maximum power point tracking (MPPT) of the solar panels and inject a sinusoidal current into the grid with FPR controller. Finally, performance of the proposed controller scheme as well as the grid-tied FCI topology are verified through simulation results.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.002
GPT teacher head0.162
Teacher spread0.160 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2019
Admission routes1
Has abstractyes

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