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Modeling and Control of VIENNA Circuit Employed Single-Phase Single-Stage Solar PV System

2023· article· en· W4386630460 on OpenAlexaff
Pemendra Kumar Pardhi, Shailendra Sharma, Ambrish Chandra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPhotovoltaic systemIntegratorSynchronization (alternating current)Grid-connected photovoltaic power systemGridTopology (electrical circuits)Electronic filterElectronic engineeringControl theory (sociology)VoltageMaximum power point trackingComputer scienceEngineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

This paper presents modeling and control of a VIENNA circuit employed in a single phase single-stage solar photovoltaic system (SPSSPVS). The topology for the proposed system consists of a half bridge voltage source converter (HBVSC), VIENNA circuit with unidirectional controlled switch and noise absorption (L-RC) filter. The proposed topology of the SPSSPVS offers to minimize the leakage current flow between PV panel to the utility grid and minimizes the component count. Inside this article, modeling and control of proposed SPSSPVS are described. The control of the system ensures the maximum photovoltaic power harvesting (MPPH) and synchronization with the utility grid. The MPPH operation is obtained using perturb and observe (P&O), however for the synchronization of HBVSC a modified third order sinusoidal integrator (mTOSSI) based band pass filter (BPF) is developed. The mTOSSI extracts the synchronization signal even if grid voltage is polluted. The performance is evaluated using the MATLAB 2022a 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.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.837
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.048
GPT teacher head0.264
Teacher spread0.216 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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