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High Resistance Grounding (HRG) and Adjustable Speed Drives (ASD)

2018· article· en· W3021129531 on OpenAlexaff
Sergio Panetta, John P. Nelson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsResearch Canada
Fundersnot available
KeywordsGroundPath (computing)Fault (geology)Power (physics)Rectifier (neural networks)Electric power systemElectrical engineeringCurrent (fluid)Computer scienceVoltagePower flowEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper provides an analysis of the use of a high resistance grounded (HRG) power system for supplying power to adjustable speed drive (ASD) loads. First, the paper provides a brief discussion on the use HRG on a power system and in particular to understand how the system works in the zero sequence plane. A path is required for the ground fault current to flow and an understanding of the path is better realized with the use of symmetrical components and the use of the zero sequence plane. Next, the paper will present a brief discussion on the use of a standard six-pulse rectifier which is commonly used on small, low and medium voltage drives. Using the basic understanding of the HRG system and the ASD, the flow of ground current from the load side of the ASD to the power system is shown. In particular, the paper will show that the ASD will most likely trip immediately for a ground fault due to the self-overload protection of the ASD whereby the system should continue to run using an HRG power system.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.196
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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