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Record W2966397563 · doi:10.1109/sgsma.2019.8784526

Hardware-in-the-Loop Use Cases for Synchrophasor Applications

2019· article· en· W2966397563 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
TopicReal-time simulation and control systems
Canadian institutionsOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsHardware-in-the-loop simulationSpoofing attackEmbedded systemComputer scienceVulnerability (computing)Global Positioning SystemConformance testingSystems engineeringReal-time computingReliability engineeringEngineeringComputer securityOperating system

Abstract

fetched live from OpenAlex

This paper presents use cases for applying Hardware-In-the-Loop (HIL) simulation in the development, testing and validation of PMU devices and synchrophasor-based applications. The use cases include PMU compliance testing, Wide Area Monitoring, Protection and Control (WAMPAC) systems testing, as well as vulnerability studies involving cyber-attacks and GPS spoofing. Real-Time Simulators (RTS) provide powerful system modeling capabilities and versatile interfaces with hardware devices and communication networks. Through the use cases, the RTS has been proven to be a useful tool for testing synchrophasor-based applications. In the paper, typical testing architectures and the advantages of using HIL are discussed.

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: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.425

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 head0.017
GPT teacher head0.243
Teacher spread0.225 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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