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Record W2787652360 · doi:10.1109/epec.2017.8286212

Seasonal reservoir management in hydro dominant power systems to enhance availability

2017· article· en· W2787652360 on OpenAlex
N. Chattha, Rajesh Karki, Fei Fang

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSpillageInflowReliability (semiconductor)Environmental scienceWater storageReliability engineeringHydroelectricityDry seasonEnergy storageWater supplyComputer scienceEnvironmental engineeringEngineeringPower (physics)Meteorology

Abstract

fetched live from OpenAlex

This paper presents reliability evaluation techniques and models for a hydro dominant system using analytical methods. Generation adequacy evaluation is done using the IEEE reliability test system modeled as a hydro dominant system, taking into account energy storage limitations of hydro plants. Seasonal analysis of storage type hydro is performed to save and have less water spillage when water inflow is high, and use it to supply load in dry months when water inflow is low and system is not capable to supply load. Studies represented in this paper specifically utilize different operating policies to save and transfer energy from wet season to dry season, and analyze its reliability benefits.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.678

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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

Quick stats

Citations5
Published2017
Admission routes1
Has abstractyes

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