Addressing Power Quality Issues through Deep Learning
Bibliographic record
Abstract
Power quality (PQ) issues refer to the different types of distortions in the sinusoidal voltage waveforms in electric power systems, also known as “power quality disturbances”. These disturbances can cause many severe problems, including losses in power supply and damage to the electrical equipment; hence, their timely detection and prevention are crucial. The first step in addressing these issues would be to identify the disturbance correctly; a total of 29 types are considered for this task. In this paper, we take some popular pre-trained Convolutional Neural Networks (CNNs) based on deep algorithms in MATLAB environment and train them to perform a new task of classifying these disturbances. An integral mathematical model is used to create a synthetic dataset for this purpose, the software version of which is publicly available. Based on the evaluation of the classification performance of these networks, we propose an automated solution to address these quality issues through deep learning.
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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) | 0.001 | 0.001 |
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".