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Record W2422560236 · doi:10.1109/icps.2016.7490259

Developing and testing a digital interconnection protection for grid-connected WECSs

2016· article· en· W2422560236 on OpenAlexaff
S. A. Saleh, Ryan Meng, Julian Meng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsInterconnectionIslandingFrame (networking)Computer scienceGridProcess (computing)Set (abstract data type)Frequency bandCoupling (piping)Power (physics)Sensitivity (control systems)Electronic engineeringElectrical engineeringElectric power systemTelecommunicationsEngineeringBandwidth (computing)PhysicsMathematics

Abstract

fetched live from OpenAlex

This paper develops and experimentally tests the performance of a digital interconnection protection for grid-connected wind energy conversion systems (WECSs). The developed protection is constructed by two frequency frames to process voltages and currents measured at the point-of-common-coupling (PCC). The first frequency frame is set to process the d - q-axis components of the instantaneous 3Φ apparent powers (sd and sq) determined at PCC. This processing of sd and sq aims to extract their high frequency sub-band contents in order to detect the islanding condition. The second frequency frame is set to extract the high frequency contents (magnitudes and phases) present in the 3Φ currents flowing through PCC. These high frequency contents provide signature information for detecting faults occurring on either side of PCC. The multi-frame digital interconnection protection is implemented for performance evaluation on two different WECSs operated in grid-connection. Performance results show accurate and reliable detection and identification of the islanding condition and faults occurring on either side of PCC. In addition, performance results show that the multi-frame digital interconnection protection has minor sensitivity to the type, structure, ratings, and levels of power delivery to the host grid.

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.

How this classification was reachedexpand

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 score0.786
Threshold uncertainty score0.348

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 communication0.0000.001
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 head0.032
GPT teacher head0.223
Teacher spread0.190 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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