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Record W2548106741 · doi:10.1109/ccece.2016.7726804

Energy management strategy of on-grid/off-grid wind energy battery-storage system

2016· article· en· W2548106741 on OpenAlexaff
Boubacar Housseini, Aimé Francis Okou, Rachid Beguenane

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsController (irrigation)Control theory (sociology)Energy storageComputer scienceBattery (electricity)GridBackupWind powerFeedback linearizationEngineeringPower (physics)Electrical engineeringControl (management)

Abstract

fetched live from OpenAlex

In on-grid/off-grid wind energy conversion systems (WECS), a backup storage is usually required in order to deal with high and low energy generation situations and to make the system more autonomous. In this paper, a nonlinear energy management control scheme for a WECS which includes a battery-storage unit with the capability to operate in grid-connected and standalone modes is proposed. The main purpose is to ensure a continuous supply of the load and to reduce the grid contribution significantly. The proposed system utilizes an unified controller contrary to the conventional methods which use a different controller for each mode of operation. In addition, the dc-link voltage and the active power are controlled directly by the battery-side bi-directional buck-boost DC/DC converter using a multi-input-multi-output (MIMO) nonlinear control design method based feedback linearization technique. The grid-side voltage source converter controls the load voltage using a PI controller. The performance of the proposed control system is evaluated in simulation. The results demonstrate that it gives high dynamic responses and zero steady-state error in response to grid power outage and load variation.

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 score0.980
Threshold uncertainty score0.510

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 head0.154
Teacher spread0.150 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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