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Record W3035861700 · doi:10.1109/tec.2020.3002953

Module Level Electronic Circuit Based PV Array for Identification and Reconfiguration of Bypass Modules

2020· article· en· W3035861700 on OpenAlexaff
Ali Faisal Murtaza, Hadeed Ahmed Sher, Kamal Al-Haddad

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2020
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsControl reconfigurationPhotovoltaic systemComputer scienceElectronic circuitPower (physics)Electronic engineeringScheme (mathematics)Series and parallel circuitsPower electronicsVoltageTopology (electrical circuits)Electrical engineeringEmbedded systemEngineering

Abstract

fetched live from OpenAlex

In this article, a novel electronic circuit based on optocoupler is designed for each module of photovoltaic (PV) array to deal with partial shading conditions. The proposed circuit not only provides electronic monitoring at the module level but also ensures the reconfiguration of bypass modules. The control framework of the proposed scheme works in two steps. In Step-1, the PV array is connected in series-parallel (SP) configuration and the power-voltage (P-V) curve is traced to search the global maximum (GM). In Step-2, bypass modules are separated from PV array through their respective electronic circuits in a decentralized control manner, and the power of bypass modules is stored in the battery. The proposed electronic circuit provides a low cost solution as it is free from expensive sensing and complex switching network. Moreover, the control operation of proposed scheme is simple. The proposed scheme is verified through computer-aided simulations and a hardware prototype of 170 W. The comparative study indicates that the proposed scheme yields more energy than past proposed solutions.

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 score0.002
Threshold uncertainty score0.007

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)0.0020.000

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 head0.031
GPT teacher head0.227
Teacher spread0.196 · 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

Citations33
Published2020
Admission routes1
Has abstractyes

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