A novel predictive control strategy for neutral-point clamped converter with harmonic spectrum shaping
Bibliographic record
Abstract
This thesis proposes a new predictive control strategy to achieve fixed-switching frequency operation for a neutral-point clamped (NPC) inverter. The classical fixed-sampling frequency finite control-set model predictive control (FSF-FCS-MPC) operates with variable switching frequency, and thus produces spread-spectrum in an output current. The classical method also suffers from high computational complexity as the number of converter voltage levels increases. To overcome these issues, a high performance variable sampling frequency finite control-set model predictive control (VSF-FCS-MPC) strategy is proposed to control the power converters. The proposed control technique combines the advantages of space vector modulation (SVM) with a newly introduced mechanics to determine the appropriate sampling frequency. With these features the major requirements such as balancing of DC-link capacitor voltages, switching frequency minimization and common-mode voltage mitigation have been achieved with simultaneous elimination of even-order and inter-harmonics in the load current harmonic spectrum. The VSF-FCS-MPC strategy for current control with decoupled active/reactive power regulation of grid-connected multilevel converter was also analyzed. Moreover, a novel DC-link voltage balancing technique is presented which eliminates the need for balancing the capacitor voltages through cost function, and thus alleviates the weighting factor design. An introduction of SVM highly reduces the calculation time by considering only adjacent vectors, rendering FCS-MPC more suitable for implementation with multi-level converters with a number of voltage levels higher than three. Finally, the proposed control technique has been validated through simulation and experimental verification and a detailed comparison of VSF-FCS-MPC with FSF-FCS-MPC and SVM is presented
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".