Hybrid Model Predictive SHM Control for Zero CMV in Three-Phase Five-/Nine-Level Packed E-Cell Inverter
Bibliographic record
Abstract
This article proposes a hybrid model predictive selective harmonic mitigation (SHM) to design a multiobjective switching control where zero common mode voltage (CMV), harmonic distortion mitigation, capacitor voltage balancing, and switching voltage-level operation are aimed in three-phase packed E-cell (PEC) inverter. SHM is developed for low frequency five-/nine-level voltages to improve power quality and achieve zero CMV. Afterward, SHM is hybridized with model predictive control (MPC) to select switching states in an online procedure and then actively balance PEC capacitor voltages. Due to MPC-based implementation of SHM, the switching voltage-level is acquired where both five-/nine-level voltages can be generated in three-phase PEC inverter to demonstrate its capability in dealing with faulty switch conditions. Hybrid model predictive SHM switching control is proven through both hardware-in-the-loop (HIL) using OPAL-RT simulator and experimental implementation using DS1202 and the results illustrate that all targeted objectives are attained for low frequency five-/nine-level three-phase PEC inverter.
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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".