A novel modeling and control method for three-phase PWM converters
Bibliographic record
Abstract
The pulse-width modulated (PWM) voltage-source converter (VSC) and current-source converter (CSC) are the building blocks of most of the switch-mode power electronic systems. Irrespective of the converter type, the controller is supposed to fulfill two objectives: (1) real power flow control which leads to the regulation of the DC quantity (DC voltage in VSC and DC current in CSC), and (2) reactive power flow control on the AC-side. The major difficulty in control is caused by the nonlinearities in the converter model. The existing control techniques are based on the design of the PI-controllers without the knowledge of the converter model, linearizing the nonlinear model, or splitting the original system into linear and nonlinear parts and dealing with them separately. In this paper, a power balance equation and nonlinear input transformation are used to derive a linear model from the original nonlinear model. Then, a decoupled state-feedback control method is applied to the new model. The accuracy of the new model and the performance of the applied control method are evaluated using the simulation results obtained from the PSCAD/EMTDC simulation package. It is shown that as a result of using the new model and applying the state-feedback control technique, the dynamics of the system are considerably improved resulting in short response times. It is also shown that the approach taken in modeling and control results in excellent results even at low switching frequencies making the scheme very suitable for high-power applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".