Estimating Complex Power Magnitudes Using a Bank of Pre-Defined PFs Embedded in ANNs
Bibliographic record
Abstract
Measuring complex power magnitudes (or apparent power) of transmission, sub-transmission, and distribution lines is a very important practice. It is used in many analysis, including: power system operation, protection, reliability, and electricity markets. Realistic networks could contain many sensors and instrument devices to provide online measurements of these variables. The communication between field, control, and supervisory levels is done through different protocols that could be integrated with different automation and energy management systems. Thus, the chance of failure to any of these different layers of online measurement always exists; which is a familiar task assigned to maintenance departments. This paper tries to solve this crucial problem by estimating these measurements without depending on any of these online devices. The idea here is to train an artificial neural network (ANN) based on a dataset created from a large number of offline power flow solutions. The numerical results show that this instruments-free power estimator (IFPE) is a highly significant and effective tool to predict any apparent power directly by just knowing the power settings of units and loads and the present status of branches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".