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Record W2554389680 · doi:10.1109/pesgm.2016.7741177

Power sharing control strategy for a no-storage hydrokinetic-diesel system in an isolated AC mini-grid

2016· article· en· W2554389680 on OpenAlexaff
Mohammadhossein Ashourianjozdani, Luiz A. C. Lopes, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsVoltage droopDiesel generatorMaximum power point trackingAutomotive engineeringPower (physics)Energy storageAutomatic frequency controlControl theory (sociology)Frequency gridRenewable energyMaximum power principlePermanent magnet synchronous generatorComputer scienceGridEngineeringVoltageDiesel fuelInverterElectrical engineeringVoltage sourceControl (management)

Abstract

fetched live from OpenAlex

The operation of diesel engine generator sets (gensets) with light loading leads to deterioration in the engine performance and efficiency. This problem is intensified in no-storage hydrokinetic-diesel systems since the genset is usually oversized to make-up for the potential shortage of renewable energy. In this paper, a new power sharing control strategy has been developed which minimizes this issue using a hydrokinetic energy conversion system (HKECS) that employs a diode AC-DC converter and voltage source inverter. The genset works with a frequency vs. power droop control. The HKECS is controlled with maximum power point tracking unless the grid frequency rises above a limit that indicates the underloading of the genset. At this point, a controlled DC load is activated so as to decrease the power injected by the HKECS thus regulating the grid frequency at this operating point. The proposed strategy is verified with a set-up with rotating machines and power electronic converters controlled via a dSPACE® system. The dynamic and steady-state operation of the system including power balancing, voltage and frequency control are presented.

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 score0.662
Threshold uncertainty score0.550

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 head0.007
GPT teacher head0.196
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

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

Citations0
Published2016
Admission routes1
Has abstractyes

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