Using Dynamic Phasors to Model a Single-Phase Active Rectifier Based on Lyapunov Current Control
Bibliographic record
Abstract
To ensure low harmonic pollution in power systems, the restrictions on total harmonic distortion (THD) produced by AC/DC converters are being made more stringent by utilities. Single-phase active rectifiers are among the power electronic conditioning devices used to ensure that loads meet the utility THD limits. Linear control schemes based on proportional-integral (PI) controllers are commonly used in single-phase active rectifiers due to ease of tuning. However, linear controllers do not provide global asymptotic stability. Lyapunov-based nonlinear control strategy enables converters to have global asymptotic stability. However, most models of Lyapunov-based single-phase active rectifiers in the literature appear in a detailed form. Detailed models require relatively large computational effort to give accurate results. In this paper, a single-phase active rectifier based on Lyapunov inner current and load current feedforward control schemes is modeled with dynamic phasors (DPs). The Lyapunov function is derived from the most dominant harmonic in each state variable. Simulation results show that the DP model is about 220 times faster than a detailed switching model. There is a close match between the DP and detailed model results. The proposed DP model is useful for the fast-paced study of multi-active-rectifier-converter systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".