Four Circuit Transmission Line Location for Inter Circuit Faults Using Fuzzy Expert System
Bibliographic record
Abstract
The inter circuit faults in four circuit power transmission line (FCPTL) have a significant influence on the conventional relay performance. Due to the special nature of faults, the protective relaying has been a challenging work. This paper investigates a fuzzy expert system (FES) approach for FCPTL to locate the inter circuit faults and to improve the accuracy of shunt faults location. This approach adopts the fundament component of current (FCC) of the FCPTL at only one end. With the goal of attaining improved inputs–outputs mapping capacity of FES for datasets, an optimization approach, i.e., Mamdani type, has been used for finding the optimal values of tuning parameters. The FES with If-Then rules has been framed for inter circuit faults location. The reported studies are performed in the LabVIEW platform using a 200 km, 500 kV, 50 Hz, FCPTL MATLAB test system. The MATLAB and LabVIEW simulation conform that the proposed approach can correctly locate faults considering various fault types and fault distances within the FCPTL. The proposed approach is easy to design and low cost for existing and new FCPTL installations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".