Fast and Reliable Geometric-Based Controller for Three-Phase PWM Rectifiers
Bibliographic record
Abstract
Three-Phase Pulse Width Modulated (PWM) converters are used in a large number of applications, such as motor drives, energy storage systems, and wind turbines, among many others. Usually, the control of this type of converter is achieved by dual-loop control structures (inner-current and outer-voltage) implemented with linear-based compensators. As a consequence, the transient response performance of the closed-loop system is limited by the dynamics of the linear compensators, leading to sluggish transient responses. In this paper, a novel geometric-based control approach for the three-phase PWM rectifier is introduced in order to improve the converter dynamics under large transients. A geometric-large signal model of the converter that describes the average natural trajectories for the operating point under different conditions is derived. Based on the natural trajectories of the converter, a closed-loop geometric based controller that computes the path that the operating point must follow to achieve the target point is developed. As a result, during transients, the operating point is able to follow a well-determined trajectory. The characteristic features of the proposed method are fast, reliable and predictable transient responses, as well as low computational cost and low-bandwidth sensing and signal conditioning stages due to the average nature of the model. Simulation and experimental results of the proposed model and control technique are provided to validate the theoretical analysis and implementation of the geometric-based large-signal controller.
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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) | 0.000 | 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".