Artificial neural network based auto-tuned PI compensator to enhance the dynamic response of the DC-link voltage in a grid-connected voltage source converter
Bibliographic record
Abstract
This paper proposes an artificial neural network (ANN) based auto-tuned PI compensator to enhance the dynamic response of the DC-link voltage in a grid-connected voltage source converter (GVSC). The converter's model is linearized using the Jacobian method and the optimized parameters of the PI compensator for many local operation regions are determined. The collected data are thereafter used as features and target for the learning process of an ANN. Two ANN structures are designed; first only one input is used, that is, the reference of the DC-link voltage. In the second case, an additional input is added, that, is the active component of the grid current. The auto-tuned PI compensator with optimized parameters provided by the ANN is tested with numerical simulations conducted on a GVSC operating as an active rectifier. The results show that the ANN based auto-tuned PI compensator provides a better dynamic response of the DC-link voltage than the conventional PI controller, that is a lower settling time with practically no overshoot.
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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) | 0.000 | 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".