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Record W2966066902 · doi:10.1109/isie.2019.8781227

Control Architecture of Solar Photovoltaic AC-Bus Microgrid with Battery Storage System

2019· article· en· W2966066902 on OpenAlexafffund
Rupak Kanti Dhar, Adel Merabet, Amer M. Y. M. Ghias, Zheng Qin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsSaint Mary's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrogridPhotovoltaic systemMaximum power point trackingEnergy storageBattery (electricity)Computer scienceTransformerGrid-connected photovoltaic power systemConvertersAutomotive engineeringEngineeringElectrical engineeringVoltagePower (physics)Inverter

Abstract

fetched live from OpenAlex

This paper presents a control architecture for a photovoltaic AC-bus microgrid with a battery storage system. In this microgrid configuration, the 2500 MVA grid is connected to the 250V AC-bus, with a PV and battery storage system. The PV power is controlled by using maximum power point tracking technique with the help of boost converter. Separate local control units are designed using voltage source converters for both PV array and battery storage. The simulation required a set of power electronic elements and electrical transformers to match the microgrid structure. The control signals are generated using proportional integral controllers. The AC-bus microgrid is designed and verified in MATLAB/Simulink environment for grid-tied and islanded mode operations. An energy management system has been developed to adjust the power sharing among the sources. The voltage and real powers in different areas, solar, grid, load and battery storage, are controlled and observed. The simulation results validate the accuracy of the controllers and the energy management system.

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: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.538

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 head0.001
GPT teacher head0.129
Teacher spread0.128 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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