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Record W2128651805 · doi:10.1109/ccece.2007.392

Design and Compliance of Frequency Drifting Islanding Detection Methods with the IEEE Standard 1547.1

2007· article· en· W2128651805 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsIslandingReliability engineeringTransient (computer programming)Computer scienceGridInverterFrequency domainRange (aeronautics)Electronic engineeringTime–frequency analysisDistributed generationEngineeringElectrical engineeringVoltageTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

Islanding is one important concern for grid connected distributed resources due to personnel and equipment safety. Preliminary design and performance assessment of inverter-resident frequency drifting islanding detection methods (IDMs) can be done by means of non-detection zones (NDZs) in a load parameter space. However, this tool only reflects the steady-state condition, that is, if the frequency of the islanded system will end up within a normal frequency range, where islanding occurs. It does not provide any information regarding the transient period, which is an important factor for the definition of the load testing points for the IEEE Std. 1547.1. Time domain simulations are used to identify critical load testing points that yield the longest run times. Then, the impact of these load values on the effectiveness of the design of frequency drifting IDMs using NDZ is discussed. As a result, an additional criterion for IEEE Std. 1547 compliance is presented.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.028
GPT teacher head0.288
Teacher spread0.260 · 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

Citations6
Published2007
Admission routes1
Has abstractyes

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