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Record W3009585314 · doi:10.1109/tpel.2020.2978672

A New Energy Management Control Method for Energy Storage Systems in Microgrids

2020· article· en· W3009585314 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

VenueIEEE Transactions on Power Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsVoltage droopControl theory (sociology)Controller (irrigation)Energy storageVoltageRange (aeronautics)Nonlinear systemMicrogridComputer scienceEnergy managementEnergy (signal processing)Stability (learning theory)Adaptive controlEngineeringControl engineeringControl (management)Voltage regulatorPower (physics)Electrical engineeringMathematics

Abstract

fetched live from OpenAlex

This article introduces a new energy management control method for energy storage systems used in dc microgrids. The proposed control method is based on an adaptive droop control algorithm that maintains the dc-bus voltage in the desired range. In the islanded mode of operation, tightly regulating the bus voltage is very challenging. The proposed control technique is based on a nonlinear droop profile with four adaptive parameters. These parameters are determined using the optimization algorithms to achieve reliable and efficient operation. The adaptive parameters enable the proposed nonlinear droop controller to tightly regulate the bus voltage during various changes in loads/sources within a dc MG. The stability of the proposed system for a very wide range of operating conditions is proved. Simulation and experimental results verify the feasibility of the proposed approach and demonstrate its superior performance compared to the conventional controllers.

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 categoriesMeta-epidemiology (narrow)
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 score0.981
Threshold uncertainty score1.000

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 head0.004
GPT teacher head0.194
Teacher spread0.190 · 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