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Record W4393118187 · doi:10.1049/rpg2.12941

A low voltage microgrid protection scheme using digital instrument transformers

2024· article· en· W4393118187 on OpenAlexaff
Jigyesh Sharma, T.S. Sidhu

Bibliographic record

VenueIET Renewable Power Generation · 2024
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMicrogridTransformerScheme (mathematics)Electrical engineeringCurrent transformerComputer scienceVoltageElectronic engineeringLow voltageEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract Modern power systems have several measurement devices for reliable, safe, and improved operation and control. Due to the high infiltration of inverter‐based resources, protecting modern power systems has become challenging. This paper proposes a new protection scheme based on the computation of ‘discrepant impedance’ using real‐time protocol services for data exchange. The sampled values of voltages and currents are transmitted to a central processing unit to implement the protection scheme. With the development of digital instrument transformers (IEC 61869‐9), the current and voltage samples are directly interfaced wired or wirelessly to the IEC 61850‐9‐2 process bus. The proposed protection scheme is validated with grid forming and grid following inverters on Consortium for Electric Reliability Technology Solutions (CERTS) microgrid network using a real‐time digital simulator. The protection scheme is independent of the type of energy sources and their control philosophy, operation modes and topology of the microgrid network.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.218
Teacher spread0.201 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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