MétaCan
Menu
Back to cohort
Record W4404317199 · doi:10.1109/tpel.2024.3496993

Bidirectional Series-Type DC Hybrid Circuit Breaker With Self-Restart Capability and Energy Regeneration

2024· article· en· W4404317199 on OpenAlexaff
Zeng Liu, Zhiming Deng, Shiqin Xiao, Chao Zhang, Yachao Yang, Yaqun Jiang, Huang Chun, Jun Wang, Z. John Shen

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsSimon Fraser University
FundersNational Natural Science Foundation of China
KeywordsCapacitorSeries (stratigraphy)Series and parallel circuitsVariable (mathematics)Computer scienceSwitched capacitorElectrical engineeringElectronic engineeringControl theory (sociology)Topology (electrical circuits)EngineeringVoltageControl (management)Mathematics

Abstract

fetched live from OpenAlex

The series hybrid circuit breaker (SHCB) is a promising solution for fault current breaking in medium and low-voltage dc distribution networks, offering fast fault breaking and low conduction losses. However, the additional circuits or devices required for charging the energy storage (ES) capacitors complicate the restart operation of the SHCB, and the dissipation of large amounts of residual magnetizing current in the transformer results in energy wastage. To address these issues, this article proposes a bidirectional energy-regeneration SHCB (ER-SHCB), which recycles the transformer's residual magnetizing current to charge the ES capacitor, enabling self-restart. This approach simplifies and accelerates the restart operation while achieving ER. In addition, the voltage injection unit can generate a controllable-polarity counter voltage, allowing for bidirectional fault current breaking. To validate the effectiveness of the ER-SHCB, 10 kV/100 A simulations and scale-down experiments were conducted. Results show that the proposed ER-SHCB, featuring self-restart capability and ER, can quickly break the operating and fault currents.

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 score0.945
Threshold uncertainty score0.959

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 head0.004
GPT teacher head0.185
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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Power ElectronicsSame topicAdvanced DC-DC ConvertersFrench-language works237,207