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Record W4285265494 · doi:10.1109/mias.2022.3161000

Centralized Protection and Control System: Are We Ready for Deployment in the Chemical, Oil, and Gas Industry?

2022· article· en· W4285265494 on OpenAlexaff
Prashant Ganoo, Sushil Joshi, Jani Valtari, Henry Niveri

Bibliographic record

VenueIEEE Industry Applications Magazine · 2022
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsABB (Canada)
FundersABB
KeywordsSoftware deploymentEngineeringIEC 61850Control (management)Systems engineeringReliability engineeringComputer securityComputer scienceAutomationSoftware engineering

Abstract

fetched live from OpenAlex

This article discusses the recent trends and customer experience with the deployment of centralized protection and control (CPC) systems mainly by utility segment customers and its feasibility in the chemical, oil, and gas (COG) segment. (See “Abbreviations Used in This Article” for a list of abbreviations used throughout.) CPC represents a new approach to protection and control in power distribution networks—centralizing all protection and control functionality in one single device on the substation level. Being International Electrotechnical Commission (IEC) 61850-compliant and ready for future upgrades with the evolving grid, it supports optimal asset management. To demonstrate the improved utilization of technologies for CPC systems, this article describes (i) a new centralized/hybrid protection and control (HPC) scheme for the medium-voltage/low-voltage (MV/LV) network, (ii) benefits to substantially improve the “ease of operation and maintenance” of industrial plants, and (iii) supplier and end customer perspective with the pros and cons of this new CPC system and its feasibility for deployment in COG industries.

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)0.002
metaresearch head score (Gemma)0.002
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0030.007
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.003

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 head0.025
GPT teacher head0.242
Teacher spread0.217 · 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
GenreCommentary

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

Citations6
Published2022
Admission routes1
Has abstractyes

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