A new technique to locate faults in distribution networks based on optimal coordination of numerical directional overcurrent relays
Bibliographic record
Abstract
During the past twenty years many methods have been presented in the literature as effective fault locators. These methods are based on impedance (time- or phasor-domain), travelling wave, or even using power quality data, superimposed components, and artificial intelligence based algorithms. Some of them are exclusively used for sub-transmission/transmission lines (overhead, underground, and joint-nodes), distribution systems, smart-grid, DC micro-grid, etc. It is well known that the primary protective devices preferred in distribution systems are directional overcurrent relays (DOCRs), because they can compromise between different design criteria (cost, reliability “security and dependability”, adequateness, speed, etc), and they can satisfy the selectivity constrains among primary and backup sets. For this reason, it is an interesting thing to design a new DOCRs-based fault locator for distribution networks using the advanced features available in the recent state of the art numerical relays. The measured operating time and the detected fault type (recorded and stored in both ends relays of the faulty branch) are used to estimate the actual location of that fault. Different techniques are implemented, including classical/logarithmic-based interpolations and linear/nonlinear regression models. Some comparative results are shown for the IEEE 8-Bus test system.
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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".