Power Quality Disturbance Detection, Classification and Correction
Bibliographic record
Abstract
For the purpose of Denoising the Power signals, the accurate estimation of the noise disturbances and the time of occurrence of the noise is needed. Once the time of occurrence of the noise is detected, it is vital to classify the type of noise so that the corrective action based on the same is done. By gauging the energy of the distorted signals at different resolutions by the virtue of the Energy Difference Multi Resolution Analysis, (EDMRA) the disturbance is identified. At different levels of resolution the distorted signal's energy distribution is found. The db4 and Morlet mother wavelet is used for resolving the noise signal in both time and frequency. The power disturbances in the signal are identified based on the difference in energy for each noise type taking the pure sinusoidal signal of 50Hz as the reference signal. The generated feature vector is fed into the input layer of a pre-trained neural network, which classifies power quality abnormalities. The adaptive filter employs an adaptive linear network to produce compensatory action for the noise signal (adaline).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".