Grey Wolf Optimization based Improved Protection of Wind Power Generation Systems
Bibliographic record
Abstract
Proper design of wind farm power system protection is an immensely challenging task for electrical power protection engineers which must be accomplished thoroughly to provide an adequate protection for power apparatus in case of fault incidence. Overcurrent relays (OCRs) are the most crucial protection tools for wind farms which are responsible for protecting power systems from faults. These relays need to be properly coordinated with each other and their settings function should be according to IEEE or IEC standards. During a fault occurrence in the wind farm especially, in the intertie section, several OCRs operate instead of a designated relay to that particular fault location, which would cause unnecessary power loss and disconnection of healthy feeders out of the wind farm that makes the situation tremendously ominous. Thus, this research proposes a novel grey wolf optimizer (GWO) based optimization technique for proper coordination of OCRs to gain improved protection of wind farms. GWO have ample advantages compared to other intelligent algorithms including, fast response, high accuracy and most notably attaining optimal solutions for nonlinear characteristics of OCRs. In this work the improvement in protection of wind farm is realized through optimizing the relay settings, reducing their operation time and time setting multiplier of each relay, improving the coordination between relays after implementation of IEC 60255-151:2009 standard. The results show that the new approach is able to achieve significant improvement in operation of OCRs at the wind farm and diminish the total operation time of the relays significantly.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".