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Record W2599053421 · doi:10.18260/1-2--20318

Development of Lab Activities for an ECE Undergraduate Renewable Energy Course

2020· article· en· W2599053421 on OpenAlexaff
Lin Zhao, Yuzhe Zhang, Jiawei Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsWestern University
Fundersnot available
KeywordsRenewable energyWind powerElectrical engineeringAutomotive engineeringPower optimizerEngineeringPhotovoltaic systemSolar energyPower electronicsMaximum power point trackingInverterVoltage

Abstract

fetched live from OpenAlex

This paper presents the development of lab activities for integration of renewable energy into electrical power systems. ECE undergraduate students can take this course in their senior year as a technical elective. This course covers variable topics including integrating of different types of renewable energy resources into electrical power systems, the basic concepts of electrical power system, and power electronics application in renewable energy technology. Hands-on experiments have been designed as the integrated part of the course. The lab activities consists of five experiments covering wind, solar, fuel cell, a stand-alone electric power system with solar and wind, and power electronics converter applications. The experiments are designed to be focused on the ECE aspect of the renewable energy and most of the experiments are openended.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.750
Threshold uncertainty score0.489

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 head0.019
GPT teacher head0.234
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2020
Admission routes1
Has abstractyes

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