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Record W4409983627 · doi:10.1049/icp.2025.1222

Novel hysteresis current control for modular multilevel converters using acceleration slope

2025· article· en· W4409983627 on OpenAlexaff
Chen Jiang, A.M. Gole, Yi Zhang

Bibliographic record

VenueIET conference proceedings. · 2025
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsRTDS Technologies (Canada)University of ManitobaManitoba Hydro
Fundersnot available
KeywordsModular designHysteresisConvertersAccelerationCurrent (fluid)Control theory (sociology)Control (management)Computer scienceElectrical engineeringEngineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

This paper presents a novel acceleration slope-based hysteresis current control strategy for modular multilevel converters (MMCs). This approach enables the MMC to function as a high-bandwidth, high-precision current source while still retaining the low inherent harmonic generation and low losses of the MMC. Unlike traditional hysteresis current control in two-level voltage-source converters (VSCs), which toggle between two output voltage levels, the MMC’s multiple voltage levels offer more flexible current slope control with fewer switching actions. The proposed control is compared to other voltage generation techniques, such as nearest-level control, showing that the resulting losses are similar to those of traditional voltage-controlled MMCs. The paper also demonstrates the application of this strategy in a STATCOM with active filtering, highlighting faster response times. Additionally, a current source type Grid Forming Converters (GFMs), e.g., Virtual Synchronous Generator (VSG), is presented, illustrating how the proposed control enhances stability in both weak and strong ac grids. The effectiveness of the proposed method and two applications are validated through electromagnetic transient (EMT) simulations and hardware-in-loop (HIL) 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.891

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 head0.045
GPT teacher head0.279
Teacher spread0.233 · 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
Published2025
Admission routes1
Has abstractyes

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