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Record W6981368340

An effective approach to retrofit existing station service distribution systems in a hydroelectrical generating station

2018· dissertation· en· W6981368340 on OpenAlexaff

Bibliographic record

VenueMspace (University of Manitoba) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicData Analysis and Archiving
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRetrofittingHydroelectricityService (business)Point (geometry)Power station
DOInot available

Abstract

fetched live from OpenAlex

A Station Service Distribution System (SSDS) plays an important role in a hydroelectric generating station. It provides a reliable power source to feed various loads inside a generating station. Throughout years of operation, SSDS equipment could fail due to aging, and replacement parts may not be readily available. The utilities are, therefore, facing many challenges in maintaining the SSDS system availability and protecting the safety of the site personnel in generating stations. This thesis proposes an effective approach and several methodologies to resolve the engineering challenges related to retrofitting an existing station service distribution system in a hydroelectrical generating station. The approach does not follow the traditional technical path, but rather a techno-economic method considering system robustness, operation cost, and safety from a long-term point of view. The approach and methodologies developed in this thesis are evaluated in a case study to validate their applicability. Conclusions show that the developed approach and methodologies effectively improve the station service distribution system in a hydroelectrical generating station. The same approach and methodologies can be applied to any generating stations facing with similar issues.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.270
Teacher spread0.249 · 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 designQualitative
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
Published2018
Admission routes1
Has abstractyes

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