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Record W2587822167 · doi:10.1109/ecce.2016.7855371

PV array voltage range extension for photovoltaic inverters using a mini-boost

2016· article· en· W2587822167 on OpenAlexaff
Emanuel Serban, Francisco Paz, Martin Ordonez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhotovoltaic systemBoost converterVoltageMaximum power point trackingSolar micro-inverterEnergy harvestingComputer scienceInverterPower (physics)Electronic engineeringElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Photovoltaic (PV) plants are designed at higher voltage and lower current operation in order to reduce the overall system installation cost. Multiple PV modules connected in series are prone to shading problems which leads to loss of energy harvested. PV inverters without a boost stage are more efficient, by removing the additional power stage, but are prone to energy harvesting loss when the array is partially shaded and the voltage drops below the minimum operating voltage. Instead, PV inverters equipped with a boost stage improve the range of conditions where energy can be harvested but add additional losses to the system reducing the efficiency. In this paper, a new converter power stage topology design and methodology optimization is proposed in the form of a mini-boost, a cost-effective and attractive solution to the traditional approach. The mini-boost extends the DC voltage range for energy harvesting under shading conditions and low irradiance conditions and only needs to process a fraction of the power leading to a cost effective and attractive solution. A comparative analysis under different irradiance levels is presented to illustrate the advantages in energy harvesting obtained with the mini-boost solution. Simulations and experimental results using a dual mini-boost dc-dc stage and 3-phase 3-level neutral point clamped inverter level are presented to validate the proposed dc-bus extension range for energy harvesting.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score0.881

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)0.0010.000

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 head0.038
GPT teacher head0.271
Teacher spread0.234 · 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
GenreMethods

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

Citations7
Published2016
Admission routes1
Has abstractyes

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