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Record W4406521453 · doi:10.1109/tec.2024.3488598

An Isolated Modular Multiport Converter for the Integration of Photovoltaic Energy Sources and Battery Storage in MVDC Networks

2025· article· en· W4406521453 on OpenAlexafffund
Sandeep Kaler, Amirnaser Yazdani

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2025
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaToronto Metropolitan University
KeywordsPhotovoltaic systemModular designEnergy storageBattery (electricity)Electrical engineeringBattery storageEngineeringComputer scienceElectronic engineeringAutomotive engineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

The intermittent nature of solar photovoltaic (PV) energy sources necessitates the use of energy storage devices, such as batteries, in electrical networks. Typically, each energy resource is integrated into the network through a separate power conversion stage. That is, clusters of PV arrays and batteries are each connected to the grid via separate power conversion stages. This paper, therefore, proposes a novel converter topology based on the dual active bridge (DAB) and modular multilevel converter (MMC) topologies that is capable of integrating both PV arrays and batteries into a medium-voltage dc (MVdc) network through a single stage. Moreover, the converter operates with a modified modulation scheme that reduces the current stress on its transformer in the presence of non-unity dc link voltage ratios. It is also shown that the converter can harvest energy from the PV arrays with maximum-power-point tracking (MPPT) over a wide range of dc voltages. All of the developed mathematical models are verified through simulation studies carried out in the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Matlab</small> & Simulink software environment. The results of the simulation are further verified by a 600-W experimental prototype.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.929
Threshold uncertainty score0.702

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)0.0000.000

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 head0.009
GPT teacher head0.205
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 teacher head, 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

Citations6
Published2025
Admission routes2
Has abstractyes

Explore more

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