Investigating Disturbance‐Induced Misoperation of Grid‐Following Inverter‐Based Resources
Bibliographic record
Abstract
ABSTRACT The rapid integration of grid‐following inverter‐based resources (GFL‐IBRs) has increased the importance of their dynamic behaviour during disturbances. Simultaneously, there are increasing number of reports about the misoperation or inadvertent disconnection of GFL‐IBRs during disturbances. This paper attempts to shed light on one of the potential root causes of disturbance‐induced misoperations of GFL‐IBRs. A framework is presented to quantify voltage drop and voltage phase angle jump that appear at the terminals of GFL‐IBRs immediately after the inception of various events in the grid such as faults, and tripping of generators and transmission lines. We demonstrate voltage drop and voltage phase angle jump in the upstream grid due to various disturbances may transform into severe voltage drop and voltage phase angle jump at the terminals of GFL‐IBRs. The combination of voltage drop and voltage phase angle jump that appear at the terminals of GFL‐IBRs is identified as one of the root causes of their misoperation. Therefore, system‐wide studies are required to evaluate the dynamic performance of GFL‐IBRs rather than sole compliance with standards. The importance of system‐wide studies is demonstrated through IEEE 39‐bus test system. The impact of voltage drop and voltage phase angle jump in the upstream grid on the dynamic performance of GFL‐IBRs is demonstrated using electromagnetic transient studies.
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 | 0.000 | 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 | 0.000 | 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".