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Analyzing the Behavior of Solid State Protection System for Dual Active Bridge in Solid State Transformers Under Short Circuit Faults

2024· article· en· W4407317571 on OpenAlexaff
Kushan Tharuka Lulbadda, Ruvini De Seram, T.S. Sidhu, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTransformerSolid-stateDual (grammatical number)Electrical engineeringPower-system protectionBridge (graph theory)Computer scienceElectronic engineeringMaterials scienceEngineeringElectric power systemEngineering physicsVoltagePhysics

Abstract

fetched live from OpenAlex

DC microgrids equipped with solid-state transformers (SST) offer a contemporary alternative to traditional low-frequency transformers (LFT) by increasing the system’s power density. The SST consists of a front-end AC/DC converter and a Dual Active Bridge (DAB) as the two major components. However, the protection of the SSTs is still under minor development as they need a fast and reliable protection system. Solid-state circuit breakers (SSCBs) are becoming more popular in DC power systems due to the need for protection at ultrafast speed. The main objective of this article is to analyze the performance of the SSCB-based protection system for DAB during short circuit faults. PLECS simulation environment has been used to simulate and analyze the protection system’s reaction to short circuit failures. A 1kW SSCB integrated DAB has been implemented in testing with actual hardware to validate the simulation results. The SSCB-based protection system achieves a remarkably high speed tripping time of 340µs. For a better understanding of the system, the behavior of the SSCB during a short circuit has been demonstrated at 50V by achieving a response time of 55.4µs. Moreover, the system is tested for different voltage levels to demonstrate the response time. This illustrates how the SSCB can safeguard SST quickly and effectively, improving safety and reliability.

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 score0.393
Threshold uncertainty score0.726

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 head0.038
GPT teacher head0.295
Teacher spread0.257 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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