A simulation study of EEPCo's medium voltage distribution feeders for technical power loss reductions: A case study of technical power losses in the outgoing feeders from Sebeta substation
Bibliographic record
Abstract
The paper presents research findings from a simulation study on technical power loss reductions in selected outgoing feeders of 15-kV to distribution transformers in one Sub-station of Ethiopia's growing power system. In the simulation process for modeling the power losses in the medium voltage transformation sub-networks, the well-known software DigSILENT has been employed. Combinations of power loss sources were considered for testing the performances of four selected feeders from Sebeta I Substation to parallel distribution transformers supplying electric power to consumers' centers mainly in south west Addis Ababa. Maximum loading of the outbound feeders from the Substation to the Medium Voltage distribution transformers located at different load centers were examined during peak load hours. Along the selected routes, with all transformers loaded to their 60% capacities, the total nominal power losses in the feeders and the transformers were also appraised. Alternative technical power loss reduction techniques have been studied with different loading considerations. Further simulation studies supported by power loss measurements and relevant applications of information communications technologies for sustainable development are also strongly recommended.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".