MétaCan
Menu
Back to cohort

Transient mode of parallel inverters connected to a hybrid microgrid: evaluation of dynamic performance considering a virtual impedance droop controller

2022· article· en· W4310971905 on OpenAlexaff
Wajdi Budahab, Mahmoud Hamouda, Kamal Al‐Haddad

Bibliographic record

VenueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMicrogridVoltage droopConvertersController (irrigation)Electrical impedanceOutput impedanceComputer sciencePower (physics)Transient (computer programming)Control theory (sociology)InverterElectronic engineeringVoltage sourceVoltageEngineeringElectrical engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

This paper investigates the effect of unintended island mode and load change on parallel inverters in a hybrid microgrid system. The power system consists of three voltage source inverters with different power ratings connected in parallel to the AC bus. Moreover, two DC/DC converters are connected to the DC bus. An interlinking converter (ILC) is used to achieve bidirectional power flow between the AC and DC buses. A virtual impedance-based droop controller is implemented achieve an accurate power-sharing between the inverters connected to the AC bus and a smooth transition between grid-connected and island modes. The main motivation behind the use of the virtual impedance is to overcome the problem caused by the mismatch of inverters’ impedances. In this regard, the performance of parallel inverters are evaluated under transient operation from grid-connected to island mode and considering load change. The results of numerical simulations confirm the effectiveness of the method and the capability of the virtual impedance controller to avoid the problem of impedance mismatch.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.226
Teacher spread0.207 · 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.

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics SocietySame topicMicrogrid Control and OptimizationFrench-language works237,207