Virtual-sensor-based control of PWM current source rectifiers
Bibliographic record
Abstract
High performance control strategies applied to pulse-width modulated current source rectifiers (PWM-CSR) require sensing of the supply currents, the input capacitor voltages, and synchronization with the AC supply voltage in addition to the DC voltage and current sensors used for protection purposes. For instance, control strategies developed to provide the necessary input line current damping-thus avoiding the need for damping resistors-and decoupled control of the active and reactive instantaneous powers, require the sensing of the supply currents and input capacitor voltages. As a result, a large number of sensors is needed and the over all reliability is therefore reduced. This paper proposes a technique based on virtual sensors to provide the required AC current and voltage values without actually sensing the electrical variables. The technique takes into account the nonlinear model of the PWM-CSR by using the information from the DC current and DC voltage sensors in combination with a linear state observer and a linear parameter identification algorithm. As a result, at least four sensors can be eliminated, while the features of the control strategy are preserved. The paper includes a complete formulation of the virtual sensor based algorithm and its application to the control of active and reactive instantaneous powers in a PWM-CSR. Results are presented to 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 | 0.000 | 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 | 0.000 |
| 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".