Robust Multi-Objective Control of VSC-Based DC-Voltage Power Port in Hybrid AC/DC Multi-Terminal Micro-Grids
Bibliographic record
Abstract
Hybrid ac/dc multi-terminal micro-grids are gaining high momentum under the smart grid paradigm to integrate renewable and clean energy resources either in the transmission or distribution systems. This paper presents a robust multi-objective controller for the voltage-source-converter (VSC)-based dc-voltage power-port in hybrid ac/dc networks. The proposed controller ensures excellent tracking performance, robust disturbance rejection, and robust stability against operating point and parameter variation with a simple fixed-parameter low-order controller. A two-degree-of-freedom control structure is proposed, where feed-forward tracking and base-line robust disturbance rejection controllers are employed to decouple disturbance rejection and tracking objectives. A disturbance rejection controller is designed, based on the singular-values (μ) synthesis approach, to achieve robustness against variation in converter operation point. Further, the effect of parametric uncertainty in the effective dc-link capacitance is mitigated by modifying the robust disturbance rejection controller, via the polynomial method, to ensure that the closed-loop poles are allocated in the pre-defined region in the complex plane even under parameter variation. A theoretical analysis and comparative simulation and experimental results are presented to show the effectiveness of the proposed control structure.
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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) | 0.000 | 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 | 0.000 |
| 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".