Enhancing Fault Ride Through Capability of Grid-Forming Virtual Synchronous Generators Using Model Predictive Control
Bibliographic record
Abstract
This paper proposes a control method for virtual synchronous generator (VSG)-based grid-forming converters to improve the stability of power electronic-dominated power grids. Overcurrent protection and fault ride-through (FRT) are crucial for the control of converter-based generators, given the limitations of power electronics components. This paper introduces a finite-set model predictive control (FS-MPC)-based FRT control technique that ensures system stability during symmetrical and asymmetrical fault types by incorporating overcurrent protection and contribution to the system voltage, while maintaining the voltage mode functionalities of the grid-forming converters. Compared to conventional control methods, the proposed MPC control technique allows for faster and improved dynamics, which makes control objectives more accessible. Additionally, the approach coordinates voltage and current control and their limitations to ensure system stability during and after fault. The proposed method also adapts to different fault types and sizes without requiring fault type detection. Simulation results in MATLAB/Simulink illustrate the effectiveness of the proposed control method in limiting the current and ensuring high power quality by providing clean sinusoidal voltage and current waveforms. Hardware-in-the-loop (HIL) real-time simulation is also presented to validate the controller performance under fault conditions.
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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 | 0.000 | 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".