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Record W2344299036 · doi:10.1109/tpwrs.2015.2504461

A Sequential Power Flow Algorithm for Islanded Hybrid AC/DC Microgrids

2015· article· en· W2344299036 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Power Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRobustness (evolution)ConvertersAlgorithmComputer scienceAC powerPower flowElectric power systemControl theory (sociology)VoltageNewton's methodPower (physics)EngineeringNonlinear systemElectrical engineering

Abstract

fetched live from OpenAlex

This paper proposes a sequential power flow algorithm for hybrid ac/dc microgrids operating in the islanded mode. Unlike in grid-connected systems, variable, rather than fixed, frequency and voltage are utilized for power coordination between the ac and dc microgrids, respectively. The main challenge is to solve the power flow problem in hybrid microgrids while considering the absence of a slack bus and the coupling between the frequency and dc voltage. In the proposed algorithm, the ac power flow is solved using the Newton-Raphson (NR) method, thereby updating the ac variables and accordingly utilizing these variables in a proposed interlinking converter model for the dc problem. This sequential algorithm is iterated until convergence. The proposed algorithm is generic and can include different operational modes not only for the distributed generation units (DGs), but also for the interlinking converters. Detailed time-domain simulations using PSCAD/EMTDC have validated the algorithm's accuracy. Its robustness and computational cost are contrasted to those of conventional algorithms.

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.

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 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: none
Teacher disagreement score0.992
Threshold uncertainty score1.000

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 head0.012
GPT teacher head0.212
Teacher spread0.199 · 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