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Record W2026821072 · doi:10.1109/tpwrs.2012.2183901

Reliability Analysis of Phasor Measurement Unit Considering Data Uncertainty

2012· article· en· W2026821072 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

VenueIEEE Transactions on Power Systems · 2012
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsPhasor measurement unitReliability (semiconductor)Fuzzy logicSensitivity (control systems)Fuzzy setReliability engineeringMarkov chainPhasorMarkov processComputer scienceMarkov modelComponent (thermodynamics)Data miningElectric power systemEngineeringMathematicsStatisticsPower (physics)Machine learningArtificial intelligence

Abstract

fetched live from OpenAlex

This paper proposes a combined statistical and fuzzy Markov method for reliability evaluation of phasor measurement unit (PMU). The major purpose is to deal with uncertainties of reliability data in PMU. The membership functions of reliability parameters can be built based on statistics and fuzzy set theory. The fuzzy hierarchical Markov models are presented to quantify membership functions of multiple reliability indices of the entire PMU. A fuzzy sensitivity analysis index is developed to estimate the effects of parameter uncertainties on the uncertainty of PMU reliability and identify the most sensitive component(s). Numerical results are provided to demonstrate the effectiveness of the proposed techniques.

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 categoriesnone
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 score0.983
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.073
GPT teacher head0.271
Teacher spread0.198 · 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