Dual two-level and three-level inverter topologies: modulation and control strategies
Bibliographic record
Abstract
As the proliferation of electric vehicles becomes more prevalent in the transportation sector, power electronic traction inverters used in these systems have been critically evaluated.With the thrust on improving inverter efficiency, reliability and reduced passive components, different variants of inverter topologies have been previously proposed and investigated.Amongst them, dual inverter topologies represent an emerging trend that has experienced growth.This thesis investigates the modulation and control strategies of dual inverter topologies using conventional two-level and three-level T-type voltage-source converters for traction applications.The thesis includes an overview of firstly, the different variants of traction inverter topologies along with the advantages and disadvantages associated with them.The advantages and research potential of the dual inverter topology is considered.Secondly, an interleaved modulation strategy for the dual two-level inverter topology is discussed which improves the performance of the passive DC-link capacitor of the inverters.Thirdly, a dual three-level T-type inverter topology is proposed which reduces the DC-link capacitor voltage balancing problem associated with a conventional three-level inverter.Additionally, the topology decouples modulation strategies to reduce switching losses from voltage balancing strategies.Moreover, three different modulation strategies for three-level inverters are proposed.Firstly, a discontinuous modulation strategy is presented which improves loss and thermal distribution of the three-level T-type inverter switching devices at lower modulation indices.The strategy entails the inverter to increase its current throughput around 30% or utilize up to 35% higher switching frequency.Secondly, a three-level generalized discontinuous modulation algorithm is proposed which enables around 50% switching loss reduction at all operating power factors.Thirdly, sub-fundamental cycle current ripple of a three-level inverter is analyzed and a variable switching frequency strategy is presented which helps in 10-20% inverter switching loss reduction.First and foremost, I would like to express my deepest gratitude to my supervisor Prof. Geza Joos for all his guidance, support and advice during my PhD studies.I have learned a lot from his critical thinking, technical knowledge, and precision, enabling me to handle and develop my capabilities as an individual.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".