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Record W2013044318 · doi:10.1109/pesmg.2013.6672249

Partial discharge impulsive noise in electricity substations and the impact on 2.4 GHz and 915 MHz ZigBee communications

2013· article· en· W2013044318 on OpenAlexaff
Jia Jia, Julian Meng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPartial dischargeWirelessNoise (video)NeuRFonElectricityElectrical engineeringInterference (communication)Electronic engineeringComputer scienceVoltageEngineeringTelecommunicationsWireless networkKey distribution in wireless sensor networks

Abstract

fetched live from OpenAlex

An investigation of the performance of ZigBee systems in high voltage electricity substations is described in this paper. The ZigBee wireless platform is a cost-efficient wireless networking system recently for the purpose of monitoring substation components in electric substations. Although this system has some inherent resistance to interference given its spread spectrum technology, impulsive noise with a short duration and a strong energy content caused by partial discharge (PD) of a dielectric breakdown can degrade the communication quality of ZigBee nodes. In this paper, a novel statistical model of substation PD impulsive noise is proposed and the impact of this impulsive noise on the ZigBee 2.4 GHz and 915 MHz frequency bands is evaluated. Although our results show the 2.4 GHz ZigBee is more resistant to PD impulsive noise in electricity substations, it may be advantageous to deploy 915 MHz ZigBee if PD detection and concurrent telemetry data collection are desired.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.314

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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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 designObservational
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

Citations4
Published2013
Admission routes1
Has abstractyes

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