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Record W2970778334 · doi:10.1109/mercon.2019.8818765

SoC Based Multi-Mode Battery Energy Management System for DC Microgrids

2019· article· en· W2970778334 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
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBackupBattery (electricity)Renewable energyState of chargeComputer scienceEnergy storageEnergy management systemPower managementAutomotive engineeringEnergy managementSupercapacitorMicrogridVoltagePower (physics)Reliability engineeringEngineeringElectrical engineeringEnergy (signal processing)Capacitance

Abstract

fetched live from OpenAlex

Renewable based DC microgrids are being widely deployed due to its increased efficiency compared to AC networks. An energy storage system helps to cater power flow imbalances due to the intermittent nature of renewable energy sources and varying load conditions. An adaptive Battery Energy Management System (BEMS) to ensure efficient operation of the battery storage system is proposed in this study. The state of charge (SoC) level management is of great importance for the prolong battery life, minimizing the capacity fade and avoiding over draining of the battery storage. Monitoring SoC level, DC bus voltage regulation and ability to provide backup power are the main considerations in designing the proposed BEMS. Performance of the proposed control algorithm was evaluated using PSCAD/EMTDC simulation results, and is presented in this paper.

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: Methods · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.483

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 head0.005
GPT teacher head0.180
Teacher spread0.176 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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