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Record W4400033780 · doi:10.1109/tpwrd.2024.3419100

Investigation of Cascaded Commutation Failures in Multiple LCC-HVdc Inverters Caused by Rectifier Side AC Grid Faults

2024· article· en· W4400033780 on OpenAlexaff
Hao Xiao, Jhair S. Acosta, A.M. Gole, Xianzhong Duan, Yi Zhang

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2024
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsRTDS Technologies (Canada)University of Manitoba
Fundersnot available
KeywordsCommutationRectifier (neural networks)ThyristorGridElectrical engineeringControl theory (sociology)Electronic engineeringEngineeringComputer scienceVoltageMathematicsControl (management)

Abstract

fetched live from OpenAlex

In earlier work, cascaded commutation failure (CF) in multiple LCC-HVdc inverters of the multi-infeed system has been attributed to inverter side ac grid faults. However, this paper discusses a phenomenon where the cascaded CF is caused by rectifier side ac faults. The risk of this phenomenon is evaluated considering various fault inception instants, types, durations, and severities by electromagnetic transient simulations. The underlying mechanism of the phenomenon is identified by calculating the area under the commutating voltage curve. It is found that the commutating voltage waveform distortion arising from asymmetrical operation of the primary inverter after the fault clearance causes the initial CF. This CF subsequently introduces significant low-order harmonics into common inverter side ac grid, which propagate to the remote inverter ac bus and consequently induce the cascaded CF. It is also shown that if the tie-line impedance between inverter ac buses limits the propagation of low-order harmonics, the cascaded CF can be avoided. The dependency of the phenomenon on ac grid parameters is further quantified using the CF immunity index (CFII). It is inferred that the phenomenon is alleviated with the increased rectifier and inverter side ac grid strength as well as tie-line impedance magnitude at fundamental frequency.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.211
Teacher spread0.194 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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