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Record W2792803562 · doi:10.1109/tia.2017.2778162

A Modified Bus-Split Method for Aggregating Distributed Generation Units

2017· article· en· W2792803562 on OpenAlexaff
S. A. Saleh, Petrus Pijnenburg, Eduardo Castillo-Guerra, Liuchen Chang

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsDistributed generationInterconnectionPhotovoltaic systemAdmittanceBus networkComputer sciencePower (physics)Sensitivity (control systems)Node (physics)Electronic engineeringElectric power systemSystem busReliability engineeringEngineeringElectrical engineeringControl busRenewable energyElectrical impedance

Abstract

fetched live from OpenAlex

This paper presents the development and performance testing of a modified bus-split method for aggregating interconnected distributed generation units (DGUs). The modified bus-split method is developed by introducing power-based models for interconnected DGUs. The introduced power-based models are used to replace admittance-based models used in the original bussplit method. The developed power-based models are generalized for DGUs that are interconnected to a 1φ or a 3φ distribution network. The modified bus-split aggregation method can be beneficial for determining possible offsets of conventional power generation, as well as improving the management of peak-demand conditions. The injected power-based bus-split aggregation method is implemented for performance testing using collected data from several wind and photovoltaic energy conversion systems, which are interconnected at different locations of the distribution network. Test results demonstrate accurate and reliable aggregation without sensitivity to the interface type, power ratings, location, voltage level at the interconnection node, and/or configuration (1φ or 3φ).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.044
GPT teacher head0.285
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations12
Published2017
Admission routes1
Has abstractyes

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