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Record W4214829111 · doi:10.1109/oajpe.2022.3156761

Experimental Investigation of Inter-Phase Power Management in Residential Microgrids

2022· article· en· W4214829111 on OpenAlexafffund
Syed A. Raza, Jin Jiang

Bibliographic record

VenueIEEE Open Access Journal of Power and Energy · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransformerThree-phasePower (physics)Reliability engineeringPower qualityConvertersComputer sciencePower BalancePhase (matter)Electrical engineeringAutomotive engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

With increased installation of single-phase rooftop PV systems, in-house battery storages, and high-power plug-in loads (i.e. EVs) at single-phase residential sites, it is prevalent that more and more distribution systems are becoming severely unbalanced causing power quality problems and thermal risks at distribution substations. To solve this problem, two main strategies have been developed previously by the authors, known as intra- and inter-phase power management strategies. The latter makes use of interconnecting back-to-back converters between the phases to mitigate the phase imbalance caused by the various single-phase DG units and loads. This paper devotes laboratory evaluation of the developed schemes and demonstrate key features experimentally on inter-phase power management. Two main experiments have been carried out to confirm that the developed technique can use the surplus power capacity from one phase to support the load demand in another phase to achieve dynamic power balance. From the point view of the substation transformer, the three-phases will always appear to be balanced despite the fact that different phases can have very different local generation and load profiles.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.356

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 communication0.0000.001
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 head0.015
GPT teacher head0.299
Teacher spread0.284 · 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 designBench or experimental
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

Citations5
Published2022
Admission routes2
Has abstractyes

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