Decoupled control of PWM active-front rectifiers using only DC bus sensing
Bibliographic record
Abstract
Active front-end rectifiers based on pulse-width modulated voltage source rectifiers (PWM-VSR) are becoming the preferred topology for implementing high performance AC/DC voltage power supplies. This is due to the fact that the input AC current is fully controllable, both in amplitude and distortion, and in phase: close to unity power factor can be obtained. However, in high performance applications, a second order filter is usually introduced on the AC side to reduce the high frequency current injection into the distribution system. To avoid the use of damping resistors, active damping is used. This requires measurement of the filter capacitor voltages, in addition to sensing of the supply currents, the AC supply voltage synchronization, DC voltage for output control and DC current for protection purposes. As a result, a large number of sensors is required and the overall reliability is thus reduced. This paper proposes a complete scheme based on virtual sensors that synthesize the required AC currents and voltages without the need for measuring them. It uses the information provided by DC current and DC voltage sensors in combination with a linear reduced order state observer and a linear parameter identification algorithm. In addition, the scheme handles the nonlinearities inherent in the model of the PWM-VSR. As a result, at least four sensors can be eliminated, while the control strategy maintains an excellent performance. The paper includes a complete formulation of the virtual sensor based algorithm and its application to control the reactive power and the DC voltage of the PWM voltage source rectifier. Simulated and experimental results on a 2 kVA digital signal processor-controlled prototype confirm the validity of the theoretical considerations.
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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) | 0.001 | 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) | 0.003 | 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".