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Record W2059411722 · doi:10.1049/iet-rpg.2009.0070

Reliability assessment of a wind-power system with integrated energy storage

2010· article· en· W2059411722 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Renewable Power Generation · 2010
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsWestern University
Fundersnot available
KeywordsReliability engineeringReliability (semiconductor)Wind powerElectric power systemMATLABComputer scienceBattery (electricity)Energy storagePower (physics)Automotive engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This study looks into reliability assessment and components rating of a wind-power system with integrated battery energy storage. The system can potentially be used in remote electrification projects to mitigate the reliance on diesel generators. A reliability assessment method has been proposed in this study, based on a combination of the traditional analytical and simulation-based approaches, to enable calculation of reliability indices, required battery capacity and power rating, and power rating of the power-electronic converter of the wind-power units. The proposed method is easy to implement in the MATLAB software environment, takes into account the units forced outage rate (FOR), and also permits modelling of the grid-connected mode.

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.

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)0.001
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 score0.386
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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