Optimum Model Predictive Torque Control of PMSM Drives for PV Water Pumping Systems
Bibliographic record
Abstract
This paper presents performance optimization of model predictive torque control (MPTC) fed permanent magnet synchronous machine (PMSM) drives based on photo-voltaic (PV) systems.The inaccurate selection of proportional-integral (PI) speed controller parameters used in MPTC can deteriorate the dynamic response of the drive.These parameters have been optimized using PI and a rigid nature-inspired salp swarm algorithm (PI-SSA), and accordingly, the system stability has improved.The precise selection of the boost dc-dc converter's duty period contributes to maximizing the extracted power and increases the efficiency of the PV arrays during variations in solar radiation.The particle swarm optimization-based maximum power point tracking (PSO-MPPT) has been applied to optimize the performance of the DC power converter fed by a two-diode PV array model.The PI controller and PI-based gray wolf optimization (PI-GWO) controller performances have been evaluated by comparison with the suggested approach.The MATLAB simulation results revealed that the suggested method provides robustness to variations in speed and torque commands, improving the settling time, rise time, and overshoot dynamic response of the system, while also increasing system efficiency by mitigating motor current total harmonic distortion (THD).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".