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Record W2564917812 · doi:10.1049/iet-gtd.2016.1449

Impact of phase‐locked loop on small‐signal dynamics of the line commutated converter‐based high‐voltage direct‐current station

2017· article· en· W2564917812 on OpenAlexaff
Chunyi Guo, Chengyong Zhao, Reza Iravani, Hui Ding, Xiaolin Wang

Bibliographic record

VenueIET Generation Transmission & Distribution · 2017
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsRTDS Technologies (Canada)University of Toronto
FundersChina Scholarship Council
KeywordsPhase-locked loopLoop (graph theory)SIGNAL (programming language)Current (fluid)Line (geometry)VoltageDirect currentControl theory (sociology)Small-signal modelElectronic engineeringComputer scienceEngineeringElectrical engineeringJitterMathematics

Abstract

fetched live from OpenAlex

This study investigates the impact of phase‐locked loop (PLL) and DC‐side voltage controller on small‐signal dynamics of the high‐voltage direct‐current (HVDC) inverter station system which utilises line commutated converter (LCC) technology. The studies are conducted based on eigenanalysis of a linearised model of a study system and verified by time‐domain simulation studies in the PSCAD platform. The studies show that under weak grid conditions, e.g. short‐circuit ratio (SCR) = 1, parameters of PLL and DC‐side voltage controller of the LCC‐HVDC station can highly impact the damping of the oscillatory modes and even cause instability. The studies also show that the voltage control parameters can be selected to prevent PLL‐induced instability. The studies also (i) show that maximum available power and critical SCR are highly affected by PLL and DC‐side voltage control and (ii) propose a procedure to systematically determine these indices.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 communication0.0000.000
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 head0.027
GPT teacher head0.289
Teacher spread0.262 · 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

Citations48
Published2017
Admission routes1
Has abstractyes

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