Carrier-Based Stair Edge PWM (SEPWM) for Capacitor Balancing in Multilevel Converters With Floating Capacitors
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Bibliographic record
Abstract
Multilevel converters with floating capacitors (FCs) are widely applied in recent years in a wide range of industrial applications from high-voltage direct current systems to high power drives. However, some FC topologies lack complete FC voltage balancing capability due to the inherent topology limitation or the insufficient switching state for capacitor balancing. This will result in large ripples on capacitors at low frequency (such as fundamental frequency), limiting their performance and application, particularly, when the fundamental frequency is very low in low-speed drive operations. Typical examples of those multilevel converters are modular multilevel converters (MMC), nested neutral-point-clamped (NNPC) converter, etc. In order to improve the FC balancing for this kind of multilevel converters, this paper proposes a carrier-based pulse-width modulation (PWM) method, named stair edge PWM method. This method can obtain sufficient redundant switching states for FC voltage balancing by producing multiple levels in one PWM period. The FC voltage balancing is thus achieved at switching frequency, which can greatly reduce the capacitor voltage ripple and therefore enable the use of much smaller FCs. Meanwhile, the voltage stress on each device and low dv/dt feature are still the same as the normal multilevel operation, reserving two of the most salient features of multilevel converters. The proposed method also features much simpler and easier implementation compared with space-vector-based approaches, particularly when high-level converters are considered. An application example using a four-level NNPC converter is provided in this paper. The effectiveness and performance of the proposed method are verified by both simulation and experiment.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it