A Comparative Assessment of Dimensionality Reduction Techniques for Diagnosing Faults in Smart Grids
Bibliographic record
Abstract
Data-driven diagnostic frameworks for large-scale power grid networks usually deal with a large number of features collected by means of sparse measuring devices. As a pre-processing task, dimensionality reduction methods can improve the efficiency of data-driven diagnostic methods by extracting sets of informative and relevant features from the raw data through appropriate transformations. This work is devoted to studying the applicability of various well-known dimensionality reduction techniques in combination with four classification models in diagnosing open circuit faults in smart grids. By providing a comparative study, this work aims at finding the best combination of dimensionality reduction techniques and classification models for diagnosing faults under normal, high signal-to-noise-ratio, low sampling rate, and high fault-resistance conditions. Various fault scenarios have been simulated on the IEEE 39-bus system and a rigorous analysis of the attained results is fulfilled so as to determine the best combinations under different conditions.
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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) | 0.000 | 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 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".