A Novel Multi-vector Model Predictive Current Control of Three-Phase Active Power Filter
Bibliographic record
Abstract
This paper proposes the application of a novel finite control set model predictive control (FCS-MPC) strategy in active power filter (APF). In the process of APF compensating harmonic and reactive power, the traditional single vector model predictive current control (MPCC) has low tracking accuracy to harmonic current, while the multi-vector MPCC has the problems of complex calculation and long calculation time, a new multi-vector MPCC control method has proposed in this paper. Firstly, the harmonic reference value is transformed into d-q coordinate system, according to the sector, the slope is calculated and the action time is obtained. Six new expected vectors are synthesized from six effective vectors and zero vectors. The value function is established to loop and calculate the optimal virtual vector, which is applied to APF. Compared with single vector control and traditional multi-vector control, it has a wider vector action area and faster calculation speed. The compensation results and dynamic performance are improved. The simulation results show that the total harmonic distortion (THD) is low.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".