Switched Midpoint Modular Multilevel Converter With Third-Order Harmonic Injection, Extended Natural Balancing, and Fault-Blocking Capability
Bibliographic record
Abstract
This article considers the switched midpoint modular multilevel converter (SMPC) topology, which has been recently proposed for the integration of renewable sources and interconnections in ac-dc grids. The SMPC employs a shared flying stack of half-bridge submodules (HBSMs) per phase, which enables zero-voltage switching (ZVS) of its director switches (DSs). It reduces the number of required submodules and lowers energy storage requirements by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {17.7\% }$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {40\% }$ </tex-math></inline-formula>, respectively, compared to the conventional modular multilevel converter (MMC). This article extends the SMPC to operate with significantly higher modulation indexes by considering the third-order harmonic injection and fault-blocking capability, making it particularly attractive for reliable high-voltage direct current (HVdc) applications requiring a wide voltage variation. Other benefits of the extended SMPC include simplified harmonic current elimination required for stack voltage balancing and reduced semiconductor stress, resulting in significant loss reduction, surpassing even the most efficient competitor, i.e., HBSM-MMC. In addition, its full-bridge submodule (FBSM)-based outer stacks offer inherent dc fault protection through its capacitors’ back EMF generation and dc fault ride-through. The principles of operation and energy-balancing concepts are presented and compared with previous well-known counterparts. Finally, the simulation results and experimental evaluations using a lab-scale prototype based on OPAL-RT MMC test bench are given to validate the superiority of the converter’s performance.
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How this classification was reachedexpand
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)
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 teacher head, 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".