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VSC-HVDC System Based Ancillary Services for AC Grids

2022· article· en· W4352980724 on OpenAlexaff
Ancha Satish Kumar, Wei Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsElectric power systemGridHigh-voltage direct currentVoltage sourceComputer scienceAutomatic frequency controlAC powerReliability (semiconductor)Renewable energyReliability engineeringControl engineeringVoltagePower (physics)EngineeringElectrical engineeringTelecommunicationsDirect current

Abstract

fetched live from OpenAlex

In viewing an increased ratio of renewable resources like solar and wind farms, point-to-point Voltage Source Converter based High Voltage DC (VSC-HVDC) system commissioning are on the rise worldwide. They are becoming techno-economical solutions for integrating bulk renewable resources into significant load centers. In this scenario, it is requisite by most of the grid codes that VSC-HVDC grids need to provide ancillary services to the AC grids. Ancillary services such as active power and frequency support are crucial for the stability and reliability of the power system. Henceforth, this paper proposes a control strategy to achieve these ancillary services through the VSC-HVDC system. The proposed control method introduces the active power and frequency loops into conventional control and extracts the support from a point-to-point VSC-HVDC system. To validate the proposed control strategy, two case studies and a test system which is a point-to-point VSC-HVDC integrated into Kundur’s two-area power system, have been considered. An RT-LAB simulation platform has been used to develop and model the test system. Further, the performance of the proposed method is demonstrated in real-time by using one of the OPAL-RT simulators, OP5707.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.366

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 head0.006
GPT teacher head0.178
Teacher spread0.173 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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