Robust Control Method Based on <i>μ</i>-Synthesis Theory and Genetic Algorithm for Grid-Connected Inverter to Cope With Multiple Uncertainties Under Weak Grid
Bibliographic record
Abstract
In the renewable energy-based distributed power generation system (DPGS), the grid-connected inverter is the interface between the generation unit and the grid. Thus, the stable operation of the grid-connected inverter system is crucial. However, the stability of the grid-connected inverter system is often affected by many aspects simultaneously, such as uncertain grid impedance and control delay. To improve the robustness of the system with the multiple uncertainties, the$\mu $-synthesis theory is adopted in this article. By intelligently constructing the weighting functions, the robustness, dynamic performance, and power quality of the grid-connected inverter system can be taken into account at the same time when designing the controller. Furthermore, to avoid the designed controller being too high to be realized in practice, this article proposes to use a third-order controller, which is optimally designed by engaging the genetic algorithm (GA). Finally, a prototype is fabricated and tested. The experimental results verify the theoretical analysis and merits of the controller designed by the proposed method compared to the controller designed by the traditional method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".