A New Adaptive Logic Phase-Shift Algorithm for Anti-Islanding Protections in Inverter-Based DG Systems
Bibliographic record
Abstract
Recent developments in anti-islanding techniques have demonstrated that phase shift techniques are very effective for anti-islanding protections in inverter-based distributed generation (DG) systems. Several approaches have been proposed in the previous researches such as slide-mode frequency shift (SMS), active frequency drift (AFD), and active frequency drift with positive feedback (AFDPF) etc. The automatic phase shift (APS) method is actually a modified SMS method. It can effectively reduce the non-detection zone (NDZ) of the SMS technique by introducing an additional phase shift increment each time the frequency of the terminal voltage stabilizes. However, it is very difficult to determine a stable islanding frequency, and the APS algorithm sometimes acts slowly, even fails in certain load conditions. A new adaptive logic phase-shift (ALPS) algorithm is proposed in this paper to regulate the additional phase shift at a suspicious islanding situation and evaluate the effects of the phase shift. This algorithm can yield a quick phase shift in an islanding situation yet only produce a very small phase shift when the grid is available for inverter-based DG systems. Both simulation and experiment results have proved the robustness and effectiveness of the newly proposed anti-islanding algorithm
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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 | 0.000 | 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 | 0.001 | 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".