Fault Detection and Classification Based on Co-training of Semisupervised Machine Learning
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
This paper presents a semisupervised machine learning approach based on co-training of two classifiers for fault classification in both the transmission and the distribution systems with consideration of microgrids. Unlike previous work in which only labeled data are treated using supervised machine learning approaches, this study uses a semisupervised machine learning approach to handle both labeled and unlabeled data. In order to extract the hidden features in the current and voltage waveforms, the discrete wavelet transform is applied, while the harmony search algorithm is utilized to identify the optimal parameters of the wavelets. The performance of the proposed method was examined on both transmission and distribution test systems in a simulation environment, and also using experimental hardware. The results have shown that the proposed approach provides flexibility and adaptability in dealing with various system conditions/configurations with high accuracy. The results also have demonstrated that the proposed semisupervised approach can improve the fault classification accuracy compared to that obtained using other machine learning approaches (i.e., supervised and unsupervised) in the case of utilizing unlabeled data to build and train the classifier's model.
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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 | 0.001 |
| 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