Fault Location in Smart Grids Through Multicriteria Analysis of Group Decision Support Systems
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Bibliographic record
Abstract
This article proposes a clustering-based hierarchical framework that includes a consensus decision support system for locating faults in smart grids. Frequency measurements are initially collected by distributed frequency disturbance recorders, and then, decomposed in the time-frequency domain. Extracted time-frequency variational modes are further analyzed through statistical analysis. The resulted features are then used by the affinity propagation (AP) clustering technique to partition the power grid. The faulty partition is determined by evaluating a heuristic index, and, is then fed to a zNumber-based multicriteria group decision support system to decide on the fault location. The effect of various preferences on AP clustering has been handled by resorting to an aggregation scheme, which considers multiple criteria into account. The feasibility and effectiveness of the proposed framework have been validated through a comprehensive study on the IEEE 39-bus system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 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