A New Approach for Fault Classification in Microgrids Using Optimal Wavelet Functions Matching Pursuit
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a new approach that uses a combination of wavelet functions and machine learning for fault classification in microgrids (MGs). Particle swarm optimization is applied to identify the optimal wavelet functions combination that serves as a matching pursuit to extract the most prominent features, which are hidden in the current/voltage waveforms when applying the discrete wavelet transform. Four different classification techniques (i.e., decision tree, K-nearest neighbor, support vector machine, and Naïve Bayes) are used to automate the procedure of fault classification in MGs and their performances are statistically compared. The consortium for electric reliability technology solutions (CERTS) MG is used to exemplify the effectiveness of the proposed approach after modeling the MG system in power systems computer aided design/electromagnetic transient direct current (PSCAD/EMTDC) software package. The results are presented, discussed, and conclusions are drawn.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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