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Record W2791237832 · doi:10.1049/iet-gtd.2017.0734

Modelling and analysis of a synchronous machine‐emulated active intertying converter in hybrid AC/DC microgrids

2018· article· en· W2791237832 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

VenueIET Generation Transmission & Distribution · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of AlbertaUniversity of Waterloo
FundersUmm Al-Qura University
KeywordsComputer scienceConvertersElectrical engineeringElectronic engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

The integration of renewable energy resources into the electrical distribution systems faces several stability challenges especially in the low inertia conditions. To address these issues, this study introduces a virtual synchronous machine (VSM) control strategy for the intertying power electronic converters in the autonomous AC/DC hybrid microgrids. It is shown that the VSM‐based controller improves the system damping following the frequency disturbances and the AC/DC voltage variations. Moreover, a power management regulation topology is implemented in the active intertying converter to achieve an accurate bidirectional power flow under different loading conditions. A small‐signal state‐space model for the entire hybrid system is developed to assess the overall system performance. Time‐domain simulation results under the PSCAD/EMTDC environment are also presented to investigate the effectiveness of the proposed techniques. The introduction of the VSM control for the intertying converters in the hybrid AC/DC microgrids provides a significant improvement in the dynamic performance and increases the robustness against external disturbances.

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 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 score0.633
Threshold uncertainty score0.636

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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