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Solar Power Electronics System Design for the Future Building Laboratory at Concordia University

2023· article· en· W4386630793 on OpenAlexaffabout
Zahra Asadi, Akrem Mohamed Aljehaimi, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsRenewable energyGridAutomotive engineeringBattery (electricity)InverterElectrical engineeringCarbon footprintEngineeringPower electronicsComputer sciencePower (physics)Greenhouse gasVoltage

Abstract

fetched live from OpenAlex

Saving the earth from becoming an uninhabitable planet as a reason for global warming, can be achieved by reducing gas emissions and carbon footprint. Today, buildings account for a large portion of global energy consumption and CO2 emissions; therefore, buildings with integrated renewable energy capacity and net-zero energy technology practices can positively contribute to the global goal of reducing carbon-dioxide emissions. This research is done on a single-family detached house in Québec, which is a research laboratory built to test renewable energy integrated systems at Concordia University, Montréal. A solar power system is designed and simulated in PSIM software for this house to benefit from solar energy. The system consists of grid-connected inverters, grid-forming inverters, and batteries. Only the interactions between the grid, the batteries, and the grid-forming inverters are designed and simulated in three modes of operation. 1) Grid feeds the loads, 2) grid chargers the battery, and 3) battery feeds the loads. Design and simulation of such power systems give the reader a clear mindset on how the system performs during the actual testing of the devices and bring a more intuitive understanding of the real circuits and controllers used. For validation purposes, one mode of operation for the grid-forming inverter is performed experimentally, and the obtained results are matching the simulated 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 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 score0.981
Threshold uncertainty score0.237

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 head0.004
GPT teacher head0.166
Teacher spread0.162 · 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
Published2023
Admission routes2
Has abstractyes

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