MétaCan
Menu
Back to cohort
Record W2620605709 · doi:10.1049/iet-rpg.2016.0459

Active and reactive power control of wind farm for enhancement transient stability of multi‐machine power system using UIPC

2017· article· en· W2620605709 on OpenAlexaff
Mehdi Firouzi, Gevork B. Gharehpetian, Younes Salami

Bibliographic record

VenueIET Renewable Power Generation · 2017
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTransient (computer programming)AC powerControl theory (sociology)Wind powerStability (learning theory)Electric power systemPower (physics)Power controlComputer scienceControl (management)EngineeringPhysicsElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This study discusses the connection of wind farms (WFs) to power system through unified inter‐phase power controller (UIPC) for enhanced transient stability of the power system. The power circuit of the UIPC is based on the conventional inter‐phase power controller (IPC), which its phase‐shifting transforms are substituted by two series converters and one shunt converter. During fault condition, the WF connected through UIPC acts as STATCOM with capability of the active and reactive power control at UIPC connecting point. Based on the UIPC model and low‐voltage ride‐through requirements of the new grid codes, a control system for active and reactive powers control is proposed for enhancement transient stability of power system. The proposed approach is validated in a four‐machine two‐area test system. Power systems computer aided design (PSCAD)/EMTDC simulation results demonstrate that the UIPC provides an effective solution for enhancement of transient stability of power system including WFs.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.246
Teacher spread0.223 · 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

Citations41
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueIET Renewable Power GenerationSame topicWind Turbine Control SystemsFrench-language works237,207