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Real-Time MPSoC-Based Electrothermal Transient Simulation of Fault Tolerant MMC Topology

2020· article· en· W3113735371 on OpenAlexaff
Zhuoxuan Shen, Venkata Dinavahi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransient (computer programming)MPSoCSystem on a chipComputer scienceFault (geology)Network topologyElectronic engineeringWaveformEngineeringTopology (electrical circuits)Embedded systemElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Among different modular multilevel converter (MMC) submodule (SM) topologies, the clamp double submodule (CDSM) has the capability of dc fault current limiting and utilizes a relatively small number of switching devices. Since CDSM has a more complex circuit structure than half-bridge or full-bridge SM, it is a significant challenge for the real-time electromagnetic transient (EMT) simulation for a multiterminal dc (MTDC) system containing CDSM MMC. This paper proposes the device-level electrothermal model of CDSM for real-time EMT simulation, which can accurately present the power losses, the junction temperatures, and the switching transient waveforms of individual switches consuming more computation resources. The individual insulated-gate bipolar transistors of the CDSM MMC during fault clearance transient are evaluated from both electromagnetic and thermal perspectives, which interact with each other dynamically. To ensure the real-time performance of the proposed model, the equivalent circuit model is combined with the device-level model. The system-level and device-level waveforms during normal operation and dc fault transient for a three-terminal dc system are both presented and compared with PSCAD/EMTDC and SaberRD. The simulation system was implemented on the Xilinx Zynq UltraScale+ZCU102 multiprocessor system-on-chip (MPSoC) platform, and the results were captured by the oscilloscope in real-time.

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 score0.328
Threshold uncertainty score0.498

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 head0.010
GPT teacher head0.223
Teacher spread0.213 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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