Adaptive Intelligence-Driven Virtual Synchronous Generator Control for Enhanced Transient Stability in Multi-Terminal HVDC Systems
Bibliographic record
Abstract
The proliferation of inverter-based renewable energy resources fundamentally challenges power system stability due to diminished inertia and weakened grid-forming capabilities. This paper proposes an adaptive virtual synchronous generator control framework integrating particle swarm optimization for real-time parameter adaptation and transient stability enhancement mechanisms specifically designed for multi-terminal HVDC systems with offshore wind integration. The methodology synthesizes electromechanical dynamics modeling with multi-objective cost function minimization, achieving autonomous adjustment of virtual inertia and damping coefficients responsive to grid strength variations. Experimental validation on a 15 kW hardware-in-the-loop testbed demonstrates 64% reduction in performance cost metrics, 51% decrease in maximum angular excursion during faults, and tripled stability margins compared to conventional fixed-parameter approaches, validating the framework's efficacy for robust grid-forming operation under diverse operational scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".