Indirect MPC with Adaptive DC-Link Voltage Control for a CHB-based Shunt Active Power Filter
Bibliographic record
Abstract
The fixed dc-link voltage operation of a shunt active power filter (SAPF) produces high current ripple and affects its ability to compensate the grid harmonic currents under light loading conditions (i.e., operating at low modulation indices). In this paper, an indirect model predictive control (MPC) is proposed for a cascaded H-bridge (CHB) based SAPF to compensate the grid harmonic currents. In addition, an adaptive dc-link voltage control philosophy is proposed to adjust the net dc-link voltage depending on the harmonic current magnitude. Through the proposed method, CHB-based SAPF always operates at higher modulation indices, resulting in an improved performance and produces less current ripple, irrespective of the harmonic current magnitude. Also, the redundancy switching state selection algorithm is developed to equally distribute the net dc-link voltage between each H-bridge module in a CHB. Simulation studies are presented to study the performance of the proposed indirect MPC with adaptive dc-link voltage control philosophy for a CHB-based SAPF with current and voltage source type harmonic loads.
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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 | 0.000 | 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".