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Record W2617951656 · doi:10.1049/iet-rpg.2016.0986

Standalone fuel cell generation system with different tracking techniques: economic analysis

2017· article· en· W2617951656 on OpenAlexaff
Venkataraghavan Karunamurthy Kumaraswamy, John E. Quaicoe

Bibliographic record

VenueIET Renewable Power Generation · 2017
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsProton exchange membrane fuel cellMaximum power point trackingElectricity generationMaximum power principleFuel cellsTracking (education)Automotive engineeringComputer scienceElectricityPoint (geometry)Power (physics)Tracking systemElectric power systemPhotovoltaic systemEngineeringProcess engineeringElectrical engineeringVoltageMathematics

Abstract

fetched live from OpenAlex

The proton exchange membrane fuel cell (PEMFC) can be operated at different points such as the maximum power point (MPP) to extract maximum power and the maximum efficiency point (MEP) to operate at maximum efficiency. However, the different tracking techniques influence the cost of electricity (COE) of the fuel cell generation system. In this paper, the economic analysis of the PEMFC with the MPP tracking (MPPT) and MEP tracking (MEPT) techniques using the HOMER energy system analysis software is presented. A detailed comparison of the economic impact of the tracking techniques for ten load configurations of a standalone fuel cell generation system, which includes combined heat and power (CHP) loads is presented and discussed. Finally, based on economic considerations, a procedure to select a suitable tracking technique for particular requirements of the standalone PEMFC application is proposed. It is found that in the case of CHP configuration, the MPPT technique is the preferred technique to achieve low COE.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.805

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 communication0.0010.000
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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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