Demonstration of a Simulated Inverter Laboratory Using Unintentional Islanding Tests from IEEE 1547.1
Bibliographic record
Abstract
Electric grids with high penetration of renewable distributed energy resources (DERs) are at risk of an islanding issue if inverter-based resources (IBRs) do not detect it properly [1]. In response to this issue, many interconnection standards have been published (e.g., IEEE 1547-2018, UL1741 SB, CSA C22.3 No. 9:2020, etc.). Realistic electromagnetic transient (EMT) models of IBRs are needed by system planners, researchers, and grid operators for evaluating the implementation of these interconnection requirements. In that context, a simulated inverter laboratory (SIL) testing platform for grid codes compliance evaluation has been developed, which includes a generic DER inverter model, specifically solar inverter model with grid support functions (GSFs), ride-through (RT) capabilities and multiple islanding detection functionalities (IDFs). The SIL was configured following IEEE 1547.1 testing procedures of unintentional islanding (UI) with an inverter model which includes IEEE 1547 GSFs, RT functions, and anti-islanding (AI) capabilities. The SIL allows fast evaluation of the DER inverter using an automated platform thus reducing the evaluation time significantly. Multiple UI test cases are validated using the SIL comparing two different IDF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".