Optimal PMU placement for reverse power flow detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The integration of renewable energy sources alter the radial nature of the conventional distribution network and causes the power flow to reverse in some periods. As the voltage regulator is normally designed for unidirectional power flow, this may cause voltage violations on the distribution feeder resulting in faults and cuts. To solve this problem, monitoring of the distribution network is essential before taking any control or protection measures. From this fact emerges the importance of reverse power flow detection. In this paper, the optimal phasor measurement placement for reverse power flow detection is discussed. An extensive literature review and a comparison among a wide range of existing optimization algorithms is done. Then genetic algorithm is selected to solve this problem. Global Optimization Tool of Matlab are used to test the proposed algorithm on IEEE-14 and IEEE-39 node test feeders.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it