Three-Phase PWM Rectifier Control: Enhanced Direct Power Control with Neural Networks from Theory to Superior Reality Performance
Bibliographic record
Abstract
This paper proposes an innovative enhancement to conventional Direct Power Control (DPC) using Neural Network Control (NNC), presenting an effective alternative control method for three-phase Pulse Width Modulation (PWM) rectifiers.Traditional DPC techniques struggle with dynamic non-linearity and system uncertainties, leading to issues such as limited resolution, lack of adaptability, interpolation errors, sensitivity to noise, overshooting, and distorted grid currents.To address these challenges, the proposed NNC algorithm replaces the PI controller of the DC link voltage, hysteresis comparators for active and reactive power, and the lookup table.The NNC algorithm is distinguished by its nonlinear mapping capabilities and real-time parallel processing.The effectiveness of the proposed approach was validated through experimental and simulation results, using the DSpace DS1103 card along with MATLAB and Control Desk software.The Total Harmonic Distortion (THD) of the grid current in simulation and experimental results is recorded as 1.20% and 4.68%, respectively demonstrating significant improvements in overall system performance in both steady and transient states, proving the efficiency, robustness, and effectiveness of the NNC algorithm compared to traditional methods.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".