A Deep Learning Method Based on Bidirectional WaveNet for Voltage Sag State Estimation via Limited Monitors in Power System
Bibliographic record
Abstract
Voltage sag state estimation on the basis of a limited number of installed monitors is essential to dividing the responsibility for the voltage sag and taking corresponding measurements for improvement in voltage quality. Therefore, a deep learning methodology via bidirectional WaveNet for the voltage sag state estimation is proposed in this paper. The presented method can simultaneously estimate voltage sag state at non-monitored buses via limited monitors. Especially, the proposed deep learning method using the bidirectional WaveNet is designed to explore the long-term and long-range temporal dependencies in both the forward and backward directions. In this way, only by using original measured voltages through monitors, high accuracy for voltage sag state estimation can be achieved without restructured or redesign of the raw monitored data. An excellent advantage of the presented algorithm is that it can be implemented without system parameters or operating conditions or any other prior information. The presented methodology was verified by the IEEE 30-bus benchmark system. The experimental results illustrated that the accuracy of the voltage sag state estimation results was over 99.83%. Furthermore, a comparison among different models, including the bidirectional GRU-based model, one-way WaveNet-based model, and bidirectional WaveNet-based model, was also conducted. The results illustrated that the proposed bidirectional WaveNet-based model achieved the highest accuracy and quickest convergence speed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".