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Record W1993842719 · doi:10.1109/isie.2008.4677012

Improved correlation technique for islanding detection of inverter based distributed generation

2008· article· en· W1993842719 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 institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsIslandingInverterDistributed generationGridComputer scienceCorrelationLimit (mathematics)InterconnectionLine (geometry)Electronic engineeringControl theory (sociology)VoltageEngineeringMathematicsElectrical engineeringTelecommunicationsArtificial intelligenceRenewable energy

Abstract

fetched live from OpenAlex

Islanding detection is one of the most important R&D topics in the area of distributed generation (DG) interconnection with utility grid. For many years, different anti-islanding protection methods were developed and proposed for inverter based distributed generation. However, most of these methods have a limit for detecting islanding when multiple DGs have to be connected with one distribution line. In this paper, we present theoritical and simulation analyses of the correlation method. The principle of the correlation technique is presented and the method was investigated for different operating conditions. The critical case-studies show that this method is effective for both single and multiple grid connected systems. In addition, to improve the effectiveness of the correlation technique, a user-defined M-sequence was used to calculate the correlation function.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.203
Teacher spread0.188 · 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

Citations4
Published2008
Admission routes1
Has abstractyes

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