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Record W2970659617 · doi:10.1049/iet-gtd.2019.0653

Control strategies to improve stability of LCC‐HVDC systems with multiple MMC taps

2019· article· en· W2970659617 on OpenAlexaff
Dalu Liu, Gregory J. Kish, Sahar Pirooz Azad

Bibliographic record

VenueIET Generation Transmission & Distribution · 2019
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of WaterlooUniversity of Alberta
Fundersnot available
KeywordsStability (learning theory)Control theory (sociology)Computer scienceControl (management)Control engineeringEngineeringArtificial intelligenceMachine learning

Abstract

fetched live from OpenAlex

High‐voltage direct current (HVDC) is a proven technology for grid integration of renewable energy sources such as offshore wind farms and interconnecting distributed systems to main power grids. One economical solution for electrifying rural communities is to extract a small amount of power from existing HVDC transmission lines with power electronic converters, which is called tapping. This study analyses the feasibility and system performance of parallel tapping line‐commutated converter (LCC)‐HVDC systems with multiple full‐bridge modular multilevel converters under various fault scenarios and operating conditions. Simulation results reveal that undesirable system disturbances such as DC‐link voltage sags, DC‐link current overshoots, and the transient reduction of inverter extinction angle are imposed on the LCC‐HVDC system in the case of tap station AC side faults. Such disturbances would endanger the reliable operation of the entire LCC‐HVDC system with the parallel taps. Furthermore, this study proposes two fault mitigating schemes, i.e. a tap station current modulation controller and three supplementary controller configurations, to reduce the impact of tap station AC side faults on the LCC‐HVDC system. Both fault mitigating schemes are controller‐based solutions, which are augmented to the existing controllers by utilising only local measurements. The proposed schemes are verified through simulations in PSCAD/EMTDC.

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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.540
Threshold uncertainty score0.686

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 head0.007
GPT teacher head0.197
Teacher spread0.189 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2019
Admission routes1
Has abstractyes

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