Hybrid MPC-SHE Technique for Capacitor Voltage Balancing in Single DC-Source Three-Phase Modified PUC Inverter
Bibliographic record
Abstract
Capacitor voltage balancing is one of the major challenges of multilevel inverter topologies which must be address in the design process of the circuit topology and switching technique for all operational condition. In this paper, Selective Harmonics Elimination (SHE) has been combined with Model Predictive Control (MPC) to actively balance the dc capacitors voltages of a designed single dc -source three-phase nine-level modified PUC inverter at a fundamental switching frequency operation. According to the proposed MPC-SHE principle, switching angles are pre-calculated using an improved SHE and then are given to a designed MPC as to integrate the redundant switching states in an online approach for each voltage level to balance the dc capacitors voltages at low switching frequency operation. The hybrid MPC-SHE technique has been validated through theoretical and simulation analyses to verify the accurate dc capacitor voltage regulation of the presented single dc -source three-phase nine-level modified PUC inverter under a critical low dynamic condition.
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".