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Record W3024552621 · doi:10.1109/tsg.2020.2993944

A New Approach to Reliability Assessment and Improvement of Synchrophasor Communications in Smart Grids

2020· article· en· W3024552621 on OpenAlexafffund
Younes Seyedi, Houshang Karimi, Constant Wetté, Brunilde Sansò

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2020
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsEricsson (Canada)Polytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhasorSmart gridReliability (semiconductor)Data transmissionEngineeringTransmission (telecommunications)Fault (geology)Probabilistic logicWirelessReal-time computingComputer scienceReliability engineeringCommunications systemElectric power systemPower (physics)TelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

Wireless communications can facilitate transfer of synchrophasor data between spatially separated phasor measurement units (PMUs) and phasor data concentrators (PDCs). However, such communication systems may impose random access delay and failure on PMU channels that lead to missing synchrophasor data frames at the output of the PDC. This paper presents a new approach for online reliability assessment and improvement of synchrophasor data communications. The proposed approach involves elaborate estimation and probabilistic prediction algorithms that trigger a prioritized handover mechanism in order to minimize the number of synchrophasor data frames that are missing over successive time stamps. Extensive simulations based on the LTE communications in a low-voltage distribution feeder confirm significant performance improvement and fast fault detection under the communication link failures. This non-intrusive approach can be adopted by network operators to ensure reliable transmission of synchrophasor data to monitoring, control, and protection applications in smart grids.

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 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: none
Teacher disagreement score0.708
Threshold uncertainty score0.700

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 head0.024
GPT teacher head0.267
Teacher spread0.242 · 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 teacher head, 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

Citations18
Published2020
Admission routes2
Has abstractyes

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