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Comparative Study of Power Losses in Single and Parallel High-Power Photovoltaic Inverter Systems

2020· article· en· W3024819744 on OpenAlexaff
Edjadessamam Akoro, Gabriel Jean Philippe Tevi, Marie Emilienne Faye, Faris Hamoud, Amadou Seidou Maïga, Mamadou Lamine Doumbia

Bibliographic record

Venue2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · 2020
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsInsulated-gate bipolar transistorPhotovoltaic systemMaximum power point trackingInverterPower semiconductor devicePower (physics)Computer scienceGrid-connected photovoltaic power systemElectrical engineeringMATLABPower moduleElectronic engineeringVoltageEngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, a comparative study of power losses in single and parallel photovoltaic inverter systems is presented. The voltage source inverters (VSI) use power semiconductor as a switch. The insulated-gate bipolar transistor (IGBT) is the most used. These semiconductor devices generate significant power losses thus reducing the efficiency in high-power grid-connected photovoltaic (PV) systems. In this case, paralleling several PV inverters to share the load could improve the efficiency of the system. Using the polynomial approximation method, the losses in the IGBT module have been calculated. A theoretical comparative study in terms of power losses is first carried out and then, it would be validated by simulation using Matlab / Simulink software. The results show that, the parallel inverter structure in high power PV systems improves the efficiency of the system by reducing the power losses generated by the IGBT modules.

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: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.086
GPT teacher head0.326
Teacher spread0.240 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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