Real Time Linear Control implementation Based on Experimentally Validated Small Signal Model of a Three-Phase Three-Level Boost-Type Vienna Rectifier
Bibliographic record
Abstract
In this paper, design and implementation of a new MIMO linear control technique based on theoretically established and experimentally validated small signal model for the three-phase three-level boost-type AC/DC Vienna converter is presented. The resulted transfer functions are discretized for sake of digital controllers design. Multiple-loop control strategy is adopted and consists of inner current feedback loops, based on the straightforward looping technique that neglects interactions between the dq components of respectively control inputs and currents, and of an outer voltage loop, designed to ensure DC voltage regulation by adjusting the magnitude of the references for the inner current loops. The proposed control approach IS first simulated, using SIMULINK of Matlab, and then validated on a 1.5 kW laboratory prototype supported by the DS 1104 digital real-time controller board of dSPACE. The obtained results prove that a judicious choice of controller parameters, as well as an adequate rating of boost inductors allow meeting the IEEE standards requirements in terms of AC line current total harmonic distortion (THD) and power factor (PF). The efficiency of the proposed control technique is maintained in case of disturbances occurring on both source and load sides
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".