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Identification of Sub-Synchronous Interaction in MMC Systems using Frequency Scanning

2020· article· en· W3113524632 on OpenAlexaff
Yi Qi, Hui Ding, Yi Zhang, Xianghua Shi, A.M. Gole

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of ManitobaRTDS Technologies (Canada)
Fundersnot available
KeywordsNyquist stability criterionBode plotFrequency domainControl theory (sociology)Oscillation (cell signaling)Nyquist plotStability (learning theory)Computer scienceNyquist frequencyModular designLow-frequency oscillationCoupling (piping)SIGNAL (programming language)Frequency responseTime domainElectronic engineeringTransfer functionEngineeringMathematicsPhysicsElectric power systemBandwidth (computing)Electrical engineering

Abstract

fetched live from OpenAlex

The interaction between the Modular Multi-level Converter (MMC) and the ac system can result in oscillation, even instability in the sub-synchronous frequency range. Rather than using an analytical model of the MMC which is complicated and often ignores details of commercial proprietary controllers, this paper proposes the application of frequency scanning of the MMC modelled on a real-time digital simulator (RTDS). The frequency response of the MMC looking from the Point of Common Coupling (PCC) bus can be extracted from the scan in either the dq0 domain or the modified PNO domain, which would be discussed in detail. The system behavior can be studied using the Generalized Nyquist stability Criterion (GNC) or equivalently, the Bode plot. The approach can predict the Critical Short Circuit Ratio (CSCR) of the ac system resulting in marginal stability, as well as the oscillation frequency. The result is then validated by real-time simulation on RTDS.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 head0.024
GPT teacher head0.245
Teacher spread0.221 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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