Model-Predictive Dual-Control Loop With Improved Current-Limiting Capability for Grid-Forming Inverter Under Grid Faults
Bibliographic record
Abstract
Current-limiting capability is crucial for fault ride-through of grid-forming (GFM) inverters. Most current-limiting schemes for GFM inverters are implemented within classical linear controllers, which cannot guarantee optimal performance in case of emergencies like faults. Additionally, the inherent cascaded structure limits the bandwidth. The advanced model-predictive control (MPC) has been developed for power converters thanks to nonlinear objectives and constraints handling ability. One of the well-known MPCs, i.e., the finite-control-set MPC (FCS-MPC) has been employed to prevent overcurrent, which roughly includes a nonlinear penalization for current magnitude violation in the cost function. In this case, the cost function of FCS-MPC will go to infinity during faults at the expense of the voltage and current reference tracking ability, and thus, the power quality gets worse. Besides, the weighting factor design is usually a nontrivial task for MPC. To maintain the high bandwidth benefit of MPC and improve the power quality during faults, a model-predictive dual-control loop (MP-DCL) is proposed in this article. The proposed method involves the outer-voltage MPC loop to generate the optimal current reference. With the current-limiting factor applied, such a constrained reference will be tracked through the proposed inner-current MPC loop. The proposed MP-DCL expresses simple design benefits and ensures the GFM system recovers from fault to normal conditions smoothly with low overshoots and without oscillation. Experimental results verify the effectiveness of the proposed strategy through numerous comparisons with state-of-the-art solutions.
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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 | 0.001 |
| 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".