A new total frequency deviation algorithm for anti-islanding protection in inverter-based DG systems
Bibliographic record
Abstract
Islanding situation is a very serious problem in distributed generation (DG) system. For inverter-based DG systems, phase shift anti-islanding techniques such as slide mode shift (SMS), automatic phase shift (APS), active frequency drift (AFD), and active frequency drift with positive feedback (AFDPF) have been proposed because of their effectiveness in preventing most of the islanding cases. However, existing phase shift techniques are all based on the assumption that at a grid failure, the voltage frequency of the inverter can be driven by its output current in a desired direction, up or down, until the inverter's frequency is drifted into the over frequency relay and under frequency relay (OFR/UFR) window. However, when the quality factor of the local loads is high, traditional phase shift mechanisms may not work as desired, instead, the frequency of the inverter could oscillate around a certain frequency point after islanding occurs. This is due to the high ratio of the energy stored in and the energy consumed in the local load. To solve this problem, a total frequency deviation (TFD) in a moving time frame is introduced as the islanding index while using adaptive logic phase shift (ALPS) as the basic phase-shift motivation. The TFD value becomes significantly large after the islanding happens. The TFD value can interact with ALPS algorithm to move the frequency of the inverter continuously until it reaches the OFR/UFR tripping window. Both simulation and lab tests have demonstrated the highly effectiveness of this 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".