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Record W4206113924 · doi:10.1109/ias48185.2021.9677358

Survivability-Based Method for Assessing Impacts of Load-Side Activities on Power Systems

2021· article· en· W4206113924 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

Venue2021 IEEE Industry Applications Society Annual Meeting (IAS) · 2021
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSurvivabilityReliability engineeringElectric power systemComputer sciencePower (physics)VoltageBoundary (topology)EngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

This paper presents the development and testing of a survivability-based method for assessing possible impacts of load-side activities on power systems. The developed method is based on defining a survivability index Γ, which is formulated in terms of the difference between pre-activity and post-activity power injection into a load bus. In order to accommodate steady-state frequency and voltage tolerances, boundary values for the index Γ are defined for each load bus. The proposed survivability-based method is implemented and tested for the Barbados power system. Tests are conducted for integrating distributed generation units, and for implementing demand response at several load buses. Results of conducted tests show that the proposed method can accurately quantify the impacts of load-sides activities on the functionality of a power system. In addition, test results demonstrate that the accuracy of the survivability-based method is not affected by the type or rating of distributed generation units and/or times and durations of demand response actions.

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)0.002
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: none
Teacher disagreement score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.019
GPT teacher head0.302
Teacher spread0.282 · 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