Performance Assessment of Active Frequency Drifting Islanding Detection Methods
Bibliographic record
Abstract
Islanding detection is a mandatory feature for grid-connected inverters. The effectiveness of passive islanding detection methods (IDMs) is usually demonstrated by means of nondetection zones (NDZs) represented in a power mismatch space (/spl Delta/P versus /spl Delta/Q). Active frequency drifting IDMs have been shown to provide improved performance but their theoretical NDZ cannot be described in the /spl Delta/P versus /spl Delta/Q space for a general RLC load. This paper shows that a load parameter space based on the values of the quality factor and resonant frequency of the local load (Q/sub f/ versus f/sub 0/) can be used in these cases. It employs a single curve to represent the NDZ of frequency drifting IDMs for any RLC loads. Equations that represent NDZs of three common active IDMs in the Q/sub f/ versus f/sub 0/ load parameter space are derived and it is shown that the slip mode frequency shift and the Sandia frequency shift IDMs can be designed to guarantee islanding detection for equivalent RLC loads with a quality factor smaller than a design value. The accuracy of the NDZs is verified with simulation and experimental results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".