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Record W1990636748 · doi:10.1109/tie.2014.2362879

A Modular Multilevel Converter Pulse Generation and Capacitor Voltage Balance Method Optimized for FPGA Implementation

2014· article· en· W1990636748 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2014
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsField-programmable gate arrayModular designComputer scienceVoltageGate arrayElectronic engineeringCapacitorEmbedded systemComputer hardwareEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

To generate numerous gating signals at a fast rate, industry controllers of modular multilevel converter (MMC) usually implement the pulse generation function in field-programmable gate array (FPGA) boards. Many methods of submodule (SM) capacitor voltage balance control (VBC) require knowing the gating signals and are therefore also implemented in the same FPGA. As the number of SMs in an MMC increases, both the latency and required resources for the implementation could become too large to meet the control requirements or fit into the FPGA. Conventional methods impose a limitation on the design of large MMC. This paper presents a pulse generation and VBC method that is optimized for FPGA implementation. With least comparison operation, this method produces the same valve voltage as other modulation methods, and it removes the need for a sorting operation in VBC, which is the main difficulty in FPGA implementation. The proposed method is implemented in the FPGA-based RT-LAB real-time simulator and tested in a hardware-in-the-loop setup. The performance of this method is validated in various tests.

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.

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 score0.972
Threshold uncertainty score0.849

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 head0.032
GPT teacher head0.277
Teacher spread0.244 · 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