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Record W2521723369 · doi:10.1155/2016/4063167

Modeling and Analysis of New Multilevel Inverter for Solar Photovoltaic Power Plant

2016· article· en· W2521723369 on OpenAlexaff
Xiaoqiang Guo, Ran He, Mehdi Narimani

Bibliographic record

VenueInternational Journal of Photoenergy · 2016
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
FundersScience Fund for Distinguished Young Scholars of Hebei ProvinceChina Postdoctoral Science Foundation
KeywordsPhotovoltaic systemEMICommon-mode signalComputer scienceVoltageInverterPower (physics)Electronic engineeringMATLABRenewable energyMaximum power point trackingCapacitorElectrical engineeringElectromagnetic interferenceEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Solar photovoltaic (PV) power plant is an effective way to utilize the renewable energy sources. EMI is one of the major concerns in PV power plant. Typically, the multilevel inverters are used in high voltage PV power plant. However, the conventional multilevel inverters require more semiconductors, which complicate the circuit structure and control algorithm. In this paper, a novel five-level inverter is introduced for the high voltage PV power plant applications. The model of the inverter is analyzed. With the redundant switching states, a new modulation strategy is proposed to reduce the common-mode voltage and EMI. The proposed approach is able to eliminate the common-mode voltage; meanwhile it has the capability of balancing the capacitor voltages. The cosimulation tests with the Matlab/Simulink and S-function are carried out. The results verify the effectiveness of the proposed method.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.019
GPT teacher head0.235
Teacher spread0.216 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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