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Record W1996500070 · doi:10.1109/lescpe.2007.4437351

DSP-based Adaptive Protection for Feeders with Distributed Generations

2007· article· en· W1996500070 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 institutionsToronto Metropolitan University
Fundersnot available
KeywordsIslandingTrippingFault (geology)Digital signal processingOvercurrentComputer scienceCircuit breakerElectrical impedanceMicrogridDistributed generationRelayElectrical engineeringEngineeringVoltageElectronic engineeringPower (physics)Renewable energy

Abstract

fetched live from OpenAlex

Distributed generations (DGs) have been increasingly connected on the distribution feeders that impose challenges on traditional feeder protections. This paper proposes a new adaptive strategy for protection of distribution-system feeders connected with DGs using state-of-the-art digital signal processing (DSP) technology. The proposed strategy overcomes DGs-imposed technical challenges on feeder protection such as increase of fault current, change of prescribed fault flow paths, sympathetic tripping, unintentional islanding operation, continuous non-interruptible fault current, etc., as well as non-DG-caused problems such as undetected high-impedance ground faults. This paper shows the strategy for effective use of state-of-the-art DSP technology for real-time determination of correct protection operations for feeders with dispersed DG-connections against faults and surges resulting from lightning, equipment short-circuit, and switching of capacitors, DGs, large loads, etc.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.318

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 head0.016
GPT teacher head0.205
Teacher spread0.189 · 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
Published2007
Admission routes1
Has abstractyes

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