Heuristic curve-fitted technique for distributed generation optimisation in radial distribution feeder systems
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
Introducing distributed generation (DG) in a distribution network has considerable advantages such as reducing power loss and cost, environmental friendliness, voltage improvement, postponing system upgrades and enhancing system reliability and continuity of service. Practical application of the DG, however, proves difficult. Social, economic and political factors affect the final optimal solution. Solution techniques for DG deployment rely on optimisation methods. The technique proposed here finds the optimal location and size of the DG to minimise the total system power loss for radial distribution feeder systems by solving two independent sub-problems: (i) location and (ii) size. A sufficient sensitivity test for the first problem is suggested. Determining the optimal DG size is done using a new heuristic curve-fitted technique that reduces the search-space by selecting fewer DG-tests. Four DG sizes, which are carefully selected based on the system's total load demand percentages, are used to determine the optimal solution. To validate the proposed technique, the 33-bus and 69-bus feeder systems are examined and the results obtained by the presented technique are compared with those obtained using other competing methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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