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Record W3138291098 · doi:10.18280/jesa.540116

Instantaneous Active and Reactive Power Control Using Direct Power Control Strategy for Multilevel Multistring Inverter Fed Photovoltaic System

2021· article· en· W3138291098 on OpenAlexvenueno aff
Sarat Kumar Sahoo

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2021
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemMaximum power point trackingTotal harmonic distortionInverterAC powerRippleSpace vector modulationControl theory (sociology)Computer scienceElectronic engineeringThree-phaseEngineeringPower controlPulse-width modulationPower (physics)VoltageElectrical engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

This paper presents the Direct Power Control (DPC) strategy for Multilevel Multistring Inverter fed Photovoltaic (PV) system to control the instantaneous active and reactive power. The proposed system consists of PV strings, boost converter and three phase three level cascaded H-bridge (CHB) inverter. In multistring topology, each PV string is connected to the dc/dc converter and the distributed MPPT control algorithm is connected to the central inverter. The Space Vector Modulation (SVM) based DPC approach is used to obtain the constant switching frequency and reduced power ripple. The detailed model of the proposed system is developed in Matlab to evaluate the performance. A laboratory based prototype of the proposed system is developed to realize the system in real time. dSPACE DS1103 is used as the control interface to perform the real time implementation (RTI). Power Quality Analyser (PQ-Box 200) is used to analyse the system parameters like voltage, current and Total Harmonic Distortion (THD) of inverter. The effectiveness of the proposed system has been validated with the help of simulation and the experimental results.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.021
GPT teacher head0.240
Teacher spread0.219 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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