A Deep Learning Model via Long Short Term Memory for Voltage Sag Location in Sparsely Monitored System
Bibliographic record
Abstract
Voltage sag has already been recognized as a critical power quality issue in power system. In fact, not only economic loss but also social impact has been produced due to voltage sag. And hence, voltage sag location is of great importance to taking effective measures, evaluating power quality level, dividing responsibility and constructing harmonious power supply and consumption environment. And hence, a deep learning method via Long Short Term Memory for voltage sag location in power system, which is sparsely monitored is presented. In detail, for the presented model, the input is measured voltage through limited sensors in a sparsely monitored power system, and meanwhile, the output is the detailed line in the whole network. In this study, the data is collected via Matlab software and the algorithm is conducted through TensorFlow tool. The test results through IEEE 30-bus system illustrate that the accuracy of voltage sag location can be achieved with high accuracy.
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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 | 0.000 |
| 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) | 0.000 | 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".