Unified Fault Ride-Through Capability-Based Resilience-Aware Metrics for Grid-Forming Inverter-Based Systems
Bibliographic record
Abstract
Various fault ride-through (FRT) strategies have been proposed to constrain the maximum current during faults to levels below predefined thresholds for grid-forming (GFM) inverters, as current limiting is the primarily required FRT capability due to its direct impact on the security of inverter semiconductors and the resilience of power systems. Additionally, the latest IEEE standards and grid codes also mandate other FRT capabilities in terms of voltages, currents, and powers to further enhance the safety, stability, and resilience of power systems. However, few existing studies have comprehensively summarized these FRT capabilities and proposed effective quantification metrics to assess them. To address these gaps, this paper presents a thorough summary of the FRT capabilities of GFM inverters and introduces a set of quantification metrics to completely evaluate these capabilities, which can also indirectly quantify the FRT capabilities' impacts on power system resilience. These metrics not only enable real-time tracking but also provide a quantitative basis for comparing distinct FRT capabilities across various FRT control strategies. Furthermore, this paper also proposes a comprehensive metric that integrates the weighted contributions of all proposed quantification measures to guide the selection of the most appropriate FRT strategy. Finally, experiments in a single GFM inverter system and simulations in large-scale power systems involving four FRT strategies demonstrate the effectiveness of the proposed quantification metrics in assessing the FRT capabilities and identifying the most suitable FRT strategy of GFM inverters under grid fault conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".