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Record W2968182222 · doi:10.1109/tpwrd.2019.2933984

Evaluation of a Constant Parameter Line-Based TWFL Real-Time Testbed

2019· article· en· W2968182222 on OpenAlexaff
Hossein Chalangar, Tarek Ould‐Bachir, Keyhan Sheshyekani, Shijia Li, Jean Mahseredjian

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2019
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsOpal-Rt Technologies (Canada)Polytechnique Montréal
Fundersnot available
KeywordsTestbedConstant (computer programming)Time constantLine (geometry)Computer scienceElectrical engineeringEngineeringAerospace engineeringMathematics

Abstract

fetched live from OpenAlex

This paper presents a testbed for real-time testing of Traveling-Wave Fault Locators (TWFLs). A Field Programmable Gate Array (FPGA) is used as the main hardware platform for conducting the real-time simulation. To accurately reproduce fault-launched travelling waves, the simulated power system is executed at a 500 ns time-step. The fault locating device is the SEL-T400L relay with a sampling rate capability of 1 MHz. The simulated grid is composed of constant parameter (CP) transmission lines (TLs). The paper presents the capabilities and limitations of such an approach and assesses the suitability of the line model for testing TWFL. Firstly, off-line analyses and validation are performed using EMTP. It is found that the CP line model can be incorporated in the double-ended TWFL testing setups. For the single-ended TWFL testing, it is shown that the CP line model can introduce false transients due to ground propagation mode which can affect testing accuracy. Secondly, real-time experimental simulations are done to elaborate the importance of sub-microsecond time-steps for testing of TWFL.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.260
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations7
Published2019
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Power DeliverySame topicHigh voltage insulation and dielectric phenomenaFrench-language works237,207