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Record W2995457065 · doi:10.1049/iet-gtd.2018.6071

Reliability evaluation of power system considering wind generators coordinated with multi‐energy storage systems

2019· article· en· W2995457065 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 Generation Transmission & Distribution · 2019
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Trade, Industry and Energy
KeywordsReliability (semiconductor)Reliability engineeringWind powerElectric power systemEnergy storageComputer scienceComputer data storagePower (physics)Automotive engineeringElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This study proposes a new methodology for a probabilistic power system reliability evaluation using a Monte Carlo simulation in case of multi‐energy storage system (ESS) installed at wind farms. A large‐scale wind turbine generator (WTG) creates significant power fluctuations and effect the stability, frequency control, and then reliability of the power system. A high penetration of wind farms can result in unacceptable variations in the frequency and voltage in the power system. The significant power fluctuation impact of the WTG can, however, be reduced by installing an ESS. The proposed model can facilitate the reliability analysis and evaluation in a viewpoint of the contribution of each ESS installed at multiple wind farms integrated to a power system. The proposed method can also be used to assess the reasonable capacity of an ESS in the power system from a sensitivity analysis. A case study is demonstrated for the proposed model and methodology using a power system with similar size to the one in Jeju Island, South Korea.

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 categoriesMeta-epidemiology (narrow)
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 score0.460
Threshold uncertainty score1.000

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 head0.013
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