Sensorless nonlinear control of a three-phase/switch/ level Vienna rectifier based on a numerical reconstruction of DC and AC voltages
Bibliographic record
Abstract
In this paper, a nonlinearity-compensation control scheme is used in combination with an extended Kalman filter estimation algorithm, in order to ensure AC current shaping and DC voltages regulation for a three-phase three-wire Vienna converter. This approach aims to reduce the high number of sensors, needed by the traditional nonlinear control approach, thus reducing realization costs and improving circuit reliability. For this aim, source and DC loads voltages are numerically reconstructed by an extended Kalman filter, based on the converter averaged model. Consequently, only two current sensors may be used in the circuit, versus 10 sensors for the conventional method. A multi-loop nonlinear control technique is, then, applied to the rectifier, using the estimated voltages instead of the measured ones. The measured currents and the estimated partial DC bus voltages are controlled via inner loops. The total output DC bus voltage is regulated in an outer loop, based on power balance consideration. The proposed method is experimentally verified on a 1.5 kVA prototype of the rectifier, using the DS1104 controller board of dSPACE and real-time workshop of Matlab. It is proved that the implemented nonlinear observer exhibits high estimation precision within acceptable response time, thus ensuring very satisfactory operation of the converter in steady state.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".