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Record W2804989466 · doi:10.1109/isplc.2018.8360237

Electromagnetic compatibility of power line communications in energy storage units

2018· article· en· W2804989466 on OpenAlexaff
Gautham Prasad, Yinjia Huo, Lutz Lampe, Victor C. M. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElectromagnetic compatibilityElectromagnetic interferencePower-line communicationElectrical engineeringBroadbandRenewable energyEnergy storageEffective radiated powerCommunications systemElectronic engineeringComputer scienceEngineeringTelecommunicationsPower (physics)Physics

Abstract

fetched live from OpenAlex

Increasing integration of renewable forms of energy production has prompted a significant growth in storage technologies to address the intermittent nature of renewable energy generation. Due to the ubiquitous nature of power cables in this environment, power line communications (PLC) is a natural solution to enable robust wired communication in energy management systems. In this paper, we address electromagnetic compatibility (EMC) issues for such systems. We begin by modeling the power cables as transmitting and receiving antennas to determine the impact of radiated emissions caused by PLC on neighboring applications. By illustrating the uniqueness associated with PLC applied in an energy storage unit, we use the standardized EMC limits to determine the maximum feeding signal strength that can be allowed on battery cables. Further, we characterize the electromagnetic interference caused by the PLC signal on nearby wired communication networks, and in particular, derive the interference power spectral density levels that are radiated onto neighboring broadband PLC communications.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.261
Teacher spread0.235 · 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 designBench or experimental
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

Citations3
Published2018
Admission routes1
Has abstractyes

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