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Operation of MMC-Based VSC-HVDC System Under Geomagnetic Disturbance

2022· article· en· W4292388489 on OpenAlexaff
Hamzeh Hosseinpour, Afshin Rezaei‐Zare

Bibliographic record

Venue2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) · 2022
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsYork University
Fundersnot available
KeywordsAC powerHarmonicsTransformerGeomagnetically induced currentControl theory (sociology)Harmonic analysisPower factorGridEmtpComputer scienceElectronic engineeringVoltageEngineeringElectrical engineeringElectric power systemGeomagnetic stormPower (physics)PhysicsMathematicsControl (management)Earth's magnetic field

Abstract

fetched live from OpenAlex

This paper presents a novel analysis of the performance of an MMC-based HVDC system in the presence of a solar storm. The detailed models of an MMC and SMs are used to acquire the accurate behavior of the MMC during the GIC. The analysis demonstrates that the current harmonics arising from the DC shift in the transformer flux are distributed between the MMC and grid. A model for the distribution of the current harmonics is introduced. It is revealed that the distribution of the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> harmonic of the current is affected not only by the MMC impedance but also by the output current and voltage of the converter as well. Also, due to the unity power factor strategy in the control system of the MMC, the converter does not respond to the increase in reactive power consumption by the transformer. Consequently, a control scheme is proposed to minimize the contribution of the grid in the reactive power augment. This control scheme provides the saturated transformer with the reactive power injected by MMC. The system is implemented in EMTP-RV, and the proficiency of the proposed control scheme is verified through simulation.

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 categoriesMeta-epidemiology (narrow)
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 score0.324
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.036
GPT teacher head0.216
Teacher spread0.180 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venue2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&amp;CPS Europe)Same topicHVDC Systems and Fault ProtectionFrench-language works237,207