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Design and Simulate a 500 MW Grid-Connected PV Farm for Labrador

2022· article· en· W4283214993 on OpenAlexaffabout
Sayed Arfat Alam Quadri, Mohamad Mahdi Baalbaki, Andrew Chacko, M. Tariq Iqbal

Bibliographic record

Venue2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) · 2022
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPhotovoltaic systemGridSwitchgearMATLABTransformerSizingTransmission systemElectric power systemComputer scienceElectric power transmissionEngineeringAutomotive engineeringControl engineeringReliability engineeringVoltageElectrical engineeringTransmission (telecommunications)Power (physics)Operating system

Abstract

fetched live from OpenAlex

This paper investigates the system sizing, schema, modeling, and simulation of a 500 MW, grid-connected PV farm at a site close to the Churchill Falls Airport in Labrador. The objective is to understand the PV farm’s technical and economic feasibility. The system is sized manually and with the help of PVWatts. The outputs from this calculation and PVWatts are used to select system components, such as solar panels and MPPT inverters. The plant is divided into four blocks of 125 MW each for ease of maintenance, control, and redundancy for planned and unplanned outages. Pooling transformers, high voltage switchgear, grid transformers, and the export transmission line are sized accordingly. System performance is analyzed using NREL’s System Advisor Model (SAM) software. The grid-connected PV system, with the inverter, is modeled in Simulink (MATLAB) for dynamic simulations. Protection and control scenarios are also modeled in Simulink. The system’s output parameters are then analyzed to understand power output and stability. Finally, recommendations on feasibility and further work are discussed.

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: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.025
GPT teacher head0.268
Teacher spread0.243 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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