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Design and Techno-Economical Optimization of Wind Turbine Generator System: Under Diverse Metrological Conditions

2013· article· en· W2007359631 on OpenAlexvenueno aff
Marangi S. Mbogho, Michael Juma Saulo

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsElectricity generationRenewable energyWind powerElectricityTurbineEnvironmental scienceMains electricitySteam turbineEngineeringEnvironmental economicsPower (physics)Electrical engineeringEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

The Kenyan Electricity Supply Industry (KESI) generation mix comprises; hydro-electric, geothermal, thermal and a small contribution from wind energy. Over the years, the predominant source of electricity generation in KESI has been hydro (over 60%). However, due to climate changes in recent times hydro generation has become less reliable and largely contributed to the supply–demand mismatch. Against this background there has been a paradigm shift in energy policy towards the use of Renewable Energy sources for electricity generation. Informed by this shift of policy, this paper explores the possibility of generating electrical power from wind-at Mokowe, Lamu County, in Kenya along the Indian Ocean. The paper contends that the use of optimally designed Wind Turbine Generators (WTGs) could enhance production of electrical power at the proposed site. This in effect would significantly: improve the technical and economic performance of KESI; and impact positively on the socio-economic status of the local communities at Makowe.

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 score0.473
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.212
Teacher spread0.199 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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