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Record W2999768210 · doi:10.3390/en13020434

A Topology Synthetization Method for Single-Phase, Full-Bridge, Transformerless Inverter with Leakage Current Suppression Part I

2020· article· en· W2999768210 on OpenAlexaff
Hongliang Wang, Xiaonan Zhu, Xinwei Wei, Yan‐Fei Liu

Bibliographic record

VenueEnergies · 2020
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsNetwork topologyInverterTopology (electrical circuits)Decoupling (probability)Pulse-width modulationComputer scienceElectronic engineeringPhotovoltaic systemEngineeringVoltageElectrical engineeringControl engineering

Abstract

fetched live from OpenAlex

Single-phase full-bridge transformerless topologies, such as the H5, H6, or the highly efficient and reliable inverter concept (HERIC) topologies, are commonly used for leakage current suppression for photovoltaic (PV) applications. The main derivation methodology of full-bridge topologies has been used based on both a DC-based decoupling model and an AC-based decoupling model. However, this methodology is not suited to the search for all possible topologies, and cannot verify whether they are inclusive. Part I of this paper will propose a new topology derivation methodology based on unipolar sinusoidal pulse width modulation (USPWM) to search all possible full-bridge topologies for leakage current suppression. First of all, a unified circuit model is proposed, instead of the DC- and AC-based models. Secondly, a mathematic method called the MN principle is then proposed to search for all possible topologies, and a derivation procedure is provided. It was verified that all existing topologies could be found using the proposed method; furthermore, seven new topologies were derived. The proposed topology derivation methodology is extended to search topologies under Double-Frequency USPWM (DFUSPWM). Twenty topologies under USPWM and four topologies under DFUSPWM have been derived.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.705

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 head0.041
GPT teacher head0.280
Teacher spread0.239 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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