On-line optimal reactive power flow by energy loss minimization
Bibliographic record
Abstract
A method for online application of optimal reactive power dispatch based on total energy loss minimization (ELM) is presented. In this approach the total energy loss from the present instant over the next hour is minimized. The method uses the load forecast during this period. All the continuous and discrete control variables are adjusted on an hourly basis. During the hour, any voltage constraint violations are removed by adjusting the VArs/voltages of generators every 15 minutes. A detailed study of a sample network is given. The ELM and power loss minimization (PLM) methods are compared by using the sample network. As seen in simulation results, the voltage profile from the ELM method is more satisfactory than that from the PLM method. In addition, the total energy loss during the specified hour which is found from the ELM method is lower than that from the PLM method. Finally, the proposed method is more likely to find feasible solutions, while the PLM method can have difficulty doing so.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".