Novel Highly Precise Power Loss Estimators that Directly Solve Power Balance Equality Constraints
Bibliographic record
Abstract
Realistic network's branches (including transformers and lines) are not lossless mediums. These power losses happen due to: 1) dissipation as heat by series resistance, 2) absorption as leakage flux by series reactance, and 3) dissipation/absorption by magnetizing conductance and susceptance. Thus, knowing these essential measurements are very important issues in many power system applications and studies, such as: power system operation, protection, reliability, and electricity markets. The existing power loss estimators have many weaknesses, such as: complicated methods, many static assumptions, and dependent on the slack unit. If the existing configuration of any network is randomly changed from its base state, then these estimators may produce insignificant and unacceptable errors. More than that, some applications require to solve a set of power balance equality constraints, such as: optimal power flow, economic load dispatch, and unit commitment. This paper introduces a totally different technique that has the ability to solve all the preceding problems steadily and directly. In this approach, a large number of randomly configured offline power flow solutions are used to create a very big dataset to train artificial neural networks. Through some numerical experiments, this technique proves itself as a highly effective tool to inexpensively and precisely estimate both active and reactive power losses.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".