Synchrophasor Assisted Power Swing Detection Scheme for Wind Integrated Transmission Network
Bibliographic record
Abstract
Integration of wind-based renewable energy sources into long Extra High Voltage/Ultra High Voltage uncompensated/compensated power transmission network poses significant problems in terms of proper detection of power swings and effective discrimination between symmetrical faults and power swing situations. As the existing protection strategies are unable to detect the said circumstances, a novel technique, based on the difference between sending end and receiving end positive sequence currents angles of the transmission line, is proposed in this paper. The required data is collected with the help of Phasor Measurement Units placed on both sides of the line. The scheme results in effective discrimination between distinct faults and circumstances of asymmetrical/symmetrical power swing and achieves satisfactory outcome during current transformer saturation condition. The proposed algorithm is evaluated on the wind-integrated IEEE-9 bus system by producing power swings, various cases of faults, and faults during power swings. Validation of the suggested technique was carried out by the execution of hardware-inloop simulation on a Real-Time Digital Simulator. The achieved outcomes disclose higher sensitivity and better discriminating ability of the presented technique in comparison with those of numerous prevailing methods.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".