Online Power Quality Disturbance Classification with Recurrent Neural Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Increasing penetration of power electronic devices is resulting in power quality disturbances that are causing marked degradation in grid performance and efficiency. Although phasor measurement data is readily available at high granularity due to the advent of advanced grid monitoring capabilities, effective processing algorithms are necessary to glean in-depth insights into the disturbances that have transpired. In this paper, a novel power quality disturbance classifier is proposed for online application by leveraging on wavelet transforms and recurrent neural networks. The existing approaches requires fixed window size, and there is a fundamental trade-off between the accuracy and localization of the event. The recurrent neural network efficiently store and memorize the past information and overcomes this limitation. The proposed technique is tested on simulation data based on IEEE-1159 standards.
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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 it