Intelligent Decision Support and Fusion Models for Fault Detection and Location in Power Grids
Bibliographic record
Abstract
Computational intelligence-based diagnostic frameworks have emerged as rapidly evolving but highly efficient approaches for diagnosing faults in power grids. This work aims to build a diagnostic framework by resorting to computational intelligence techniques to improve decision-making and diagnostic accuracy. This diagnostic framework has three modules for signal processing, fault detection, and location. The signal-processing module uses the variational mode decomposition technique to extract informative time-frequency features from the voltage and frequency signals. Voltage features are then fed into the fault detection module to train a set of modular support vector machines that are used for monitoring the binary state of each node in the power grid. Once a faulty state on a node is detected, it activates the third module for identifying fault location. This module benefits from a novel zSlices-based general type-2 fuzzy fusion model for the sake of identifying the fault type as well as mitigating the false alarm rate. The exact location of the fault is then determined through a fuzzy decision support system that is equipped with a recommendation mechanism for the sake of consensus reaching. Various scenarios are simulated on the IEEE 39-bus system and on an experimental setup of a Three-Bus Two-Line transmission system, where the attained results verify the applicability, efficiency, and robustness of the proposed framework.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".