Co-optimization of Inverter Controls and Line Protection Functions for Improved Protection Reliability and System Stability
Bibliographic record
Abstract
Protection schemes for today’s power systems have been developed over many decades. These schemes assume that the power system and especially fault currents are dominated by synchronous generators. However, with the rapid deployment of renewable generation like wind and solar, power systems are increasingly being dominated by grid-following (GFL) and grid-forming (GFM) inverter-based resources (IBRs). Synchronous generators and IBRs show fundamentally different dynamics, especially during faults. This may render today’s protection schemes inadequate for future power systems. Grid-forming (GFM) inverters can be configured with different fault ride-through (FRT) functions and their interaction with system protection is not well-studied. This paper presents a framework for co-optimizing GFM FRT functions and line protection schemes for improved protection reliability and stability. The solution framework relies on a Bayesian optimization engine that actively interacts with an EMT simulator. The efficacy of our approach in finding the combination of GFM FRT and line protection functions that optimize the targeted protection and stability metrics is demonstrated on a 100% IBR test network.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".