Model Predictive Control of Distributed Generations With Feed-Forward Output Currents
Bibliographic record
Abstract
In this paper, a voltage and frequency control scheme based on model predictive control is proposed for inverter-based distributed generations (DGs). Currents injected into an off-grid system (e.g., a passive network with loads or an islanded microgrid) at the point of common coupling of the DG are considered as disturbances and used as feed-forward signals. These signals enhance the transient performance of the DG control system for a wide range of switched loads as well as for switching and operating the DG in an islanded microgrid. The stability and robustness of the proposed control scheme are analyzed and discussed. The effectiveness of the scheme is demonstrated by extensive time-domain simulations using PSCAD/EMTDC for various loads (such as balanced/imbalanced, and nonlinear and dynamic loads), fault conditions, and DG operation after switching in an islanded microgrid. Comparison of the obtained results with those of three previously developed schemes shows superiority of the proposed scheme.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".