On-line optimal reactive power flow by energy loss minimization
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
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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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) | 0.002 | 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