PI Controller Tuning Optimization for Grid-Connected VSC using Space Mapping
Bibliographic record
Abstract
Controller tuning of voltage source converter (VSC), using voltage oriented control (VOC), has a significant impact on the system stability against disturbances. To this end, optimization algorithms are sought in order to achieve optimum dynamic performance. Such algorithms are normally applied to numerical simulators that are time-intensive, which hinders the design process. This paper proposes, for the first time, space mapping (SM) optimization algorithm for controller tuning in a grid-connected VSC system. SM is a surrogate-based optimizer that utilizes a ‘coarse’ model that is less accurate, yet extremely fast, to guide the optimization of the numerical, time-intensive ‘fine’ model. The development and employment of both models are presented in this study. Subsequently, the accuracy and efficiency of the SM algorithm are assessed by simulations. It is found that SM optimization algorithm can return a quasi-optimum solution in less than half the time taken by optimizing the fine model with adequate accuracy.
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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 | 0.000 |
| 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".