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Record W2789350385 · doi:10.1109/tie.2018.2803765

Fault Ride-Through Capability of Voltage-Controlled Inverters

2018· article· en· W2789350385 on OpenAlexaff
Prasanna Piya, Mohammad Ebrahimi, Masoud Karimi-Ghartemani, S. Ali Khajehoddin

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsController (irrigation)Control theory (sociology)Fault (geology)OvercurrentTransient (computer programming)VoltageEngineeringAC powerGridVoltage optimisationComputer scienceControl engineeringControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

An auxiliary voltage controller is proposed for the fault ride-through (FRT) control of voltage-controlled inverters used in distributed energy resources. The proposed auxiliary controller can be added to different existing voltage control strategies with minimum or no need to alter the original structure of the voltage controller. The proposed controller prevents the transient overcurrent during grid faults and controls the exchange of real and reactive powers according to the governing grid standard. A fault detection algorithm is also implemented to detect the fault instant and determine the interval of fault during which the real and reactive power references are adjusted accordingly. Complete stability analysis of the proposed controller is performed through a new model. Successful performance of the proposed controller in achieving FRT requirements of German and Danish codes are illustrated by simulations. Extensive real-time and experimental results are also presented and discussed.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.217
Teacher spread0.202 · 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
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

Citations158
Published2018
Admission routes1
Has abstractyes

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