Estimating Power Flow Directions Using Off-Line PF Analysis and Artificial Neural Networks
Bibliographic record
Abstract
Real electric power systems are dynamic where many output variables could change at any time by influences of many input variables. Such these output variables are the directions of active and reactive power that flow on each branch. These directions could be identified by analyzing their online fundamental signals received from some field-mounted instrument devices. If any of these devices fail to operate or if there is any interruption in the communications and protocols of any hierarchical level of their energy management system (EMS), then some of power flow directions will not be identified. This study tries to estimate these dynamic directions without depending on their instrument devices. The technique proposed in this paper is processed through two stages: 1) creating a very big offline power flow (PF) solutions dataset by randomly changing the settings of power system components, and then 2) training artificial neural networks (ANNs) to have the ability to predict the power flow directions based on the settings of generators and loads, and the status of branches. The results show that the novel technique proposed in this paper is highly significant, and thus it could open the door wide for many other applications.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".