Power Loss Reduction Using Distributed Generation Sources Considering Protection Coordination and Harmonic Limits
Why this work is in the frame
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Bibliographic record
Abstract
Nowadays, high penetration of distributed generation (DG) sources in the distribution system has several impacts such as harmonic distortion, changes in short circuit current levels and power flow through branches. Using DG has both advantages and disadvantages. For example, supplying the loads locally would reduce network losses, while the overcurrent relays coordination might fail. In this paper, a new optimization problem is introduced to minimize distribution system losses by adding DGs as well as maintain the original protection scheme considering harmonic distortion limits. The problem is solved using Genetic Algorithm (GA) to find the optimal sizes of DGs to achieve the minimum losses in compliance with the desired constraints. Simulation studies are performed on 14-node radial distribution test system to validate the performance of the purposed method.
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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) | 0.000 | 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